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APPENDICES
to the final report for NCHRP Project 01-45,
“Models for Estimating the Effects of Pavement Condition on Vehicle Operating Costs”
APPENDIX A
FUEL CONSUMPTION MODELS
A-1
A1 - IDENTIFICATION AND EVALUATION OF FUEL
CONSUMPTION MODELS
This appendix summarizes the detailed equations and relationships of current fuel
consumption models. These models were also evaluated regarding their applicability to the
paved surfaces and traffic and environmental conditions encountered in the United States that are
capable of addressing the full range of vehicle types.
EXISTING VOC MODELS
The VOC models can be grouped into empirical- and mechanistic-based models. The
only available U.S. VOC models are those of the Texas Research and Development Foundation
(TRDF) developed by Zaniewski et al; an updated version of this model is in the
MicroBENCOST VOC module (McFarland et al., 1993). The most recent VOC models have
been developed outside the U.S., and are mechanistic-empirical in nature. The relevant models
are:
• The World Bank’s HDM 3 and 4 VOC models; • Australian NIMPAC VOC models (adopted in HDM 3 with some modifications) and
ARFCOM model of fuel consumption (adopted in HDM 4 with some modifications); • Saskatchewan VOC models; • Swedish VETO models.
As mentioned earlier, the VOC are a function of the following six categories of costs:
1. Fuel consumption costs
2. Oil consumption costs
3. Tire consumption costs
4. Repair and maintenance costs
5. Capital costs (depreciation and interest)
6. License and insurance costs
Based on the literature review, only fuel consumption, tire wear and repair and
maintenance costs are affected by pavement conditions. Therefore, the focus of this research was
A-2
only on estimating these costs. The models are either empirical or mechanistic-empirical models.
This section briefly reviews some of the major fuel consumption models (identified by the
research team) that have been developed.
Empirical Models
Early work conducted in the US established charts and tables for calculating fuel
consumption cost based on vehicle class only (Winfrey, 1969). Later Zaniewski et al. (1982)
updated the fuel consumption tables based on empirical models derived from experimental field
trials. Although this is the most comprehensive study conducted in the US to date, it did not treat
all aspects of the problem. While fuel consumption tests were carried out for idling, acceleration,
deceleration, and constant speed driving, the effect of pavement conditions on VOCs was only
considered in the constant speed case. Constant speed mode was used for most of the
experimental effort in these field trials, which also tested the effect of speed, grade, surface type,
and pavement condition. No tests were carried out for larger truck combinations, and relations
were assumed for a 3-S2 unit. Also the fuel consumption values were based on only one test
vehicle in each class, except for the medium size car, where two identical vehicles were used so
that the variance between the two identical cars could be used in the statistical analysis.
However, the tests on the effect of pavement conditions showed no significant difference
between the two identical cars, which means it was not necessary to do these tests after all
(Zaniewski et al., 1982). According to Zaniewski’s tables and charts, pavement conditions had a
minor effect on fuel consumption. They found that grade, curvature, and speed were the major
factors that affect fuel consumption.
The US Department of Transportation (USDOT) recently conducted a study to
investigate highway effects on vehicle performance (Klaubert, 2001). The study developed the
following fuel consumption model based on regression analysis:
1FCFE
= ( A.1)
2
cTFE a b = +
( A.2)
A-3
where:
FC = Fuel consumption in L/km
FE = Fuel economy (km/L)
T = Engine torque (N-m)
a, b, c = regression coefficients, depending on gear number
Mechanistic-Empirical Models
Mechanistic models predict that the fuel consumption of a vehicle is proportional to the
forces acting on the vehicle. Thus, by quantifying the magnitude of the forces opposing motion
one can establish the fuel consumption. Mechanistic models are an improvement over empirical
models since they can allow for changes in the vehicle characteristics and are inherently more
flexible when trying to apply the models to different conditions. Some of the most recent
mechanistic fuel consumption models are given below. The research team noted that most of the
models are derived from earlier ones. The following models are discussed chronologically.
The South African fuel consumption model considers that the fuel consumption is
proportional to the total energy requirements that are governed by the total engine power and an
engine efficiency factor (Bester, 1981). Equation (A.3) shows the form of this model.
1000 totPFCv
β= ( A.3)
where:
FC = Fuel consumption in mL/km
β = Fuel efficiency factor in ml/kW/s or mL/KJ
Ptot = Total power requirement in kW
v = Vehicle velocity in m/s
The South African model assumes that the fuel efficiency of the vehicle is independent
from the driving mode. However, a number of studies that were conducted in the early 1980’s in
Australia to model fuel consumption found that the fuel efficiency increases in the acceleration
case (Biggs, 1987). An improved mechanistic model was then developed to predict fuel
consumption using the following relationship.
A-4
22
1000trMa vIFC P βα β= + + ( A.4)
where:
α = Steady state fuel consumption in mL/s
β = Steady state fuel efficiency parameter in mL/(KJm/s)
β2 = Acceleration fuel efficiency parameter in mL/(KJm/s2)
M = Vehicle mass in kg
v = Vehicle velocity in m/s
Some studies in the later 1980’s in Australia found that the fuel efficiency is not only a
function of tractive power but also a function of the engine power. The following mechanistic
model (ARRB ARFCOM model) was developed to predict the fuel consumption as a function of
the input (engine) and output power. The general form of the model is described by the following
equations (Biggs, 1988):
( )( )PengPoutIFC −= *,max βα ( A.5)
( )max/*1 PPehp outb += ββ ( A.6)
where:
Pout = The total output power of the engine required to provide tractive force and run the accessories (KW)
Peng = The power required to run the engine (KW)
Pmax = The rated power or the maximum power (KW)
βb = Base fuel efficiency parameter in mL/(KJm/s)
ehp = Proportionate decrease in efficiency at high output power
The model predicts the engine and accessories power as a function of the engine speed.
These relationships are from a regression analysis and are given below as Equations (A.7) and
(A.8).
A-5
2.5
max* *RPM RPMPacs EALC ECFLC PTRPM TRPM
= +
( A.7)
2
1000*
+=
RPMbengcengPeng ( A.8)
where:
EALC = The accessory load constant (KW)
ECFLC = The cooling fan constant
Pmax = The rated power or the maximum power (KW)
RPM = Engine speed
TRPM = Load governed maximum engine speed
ceng = Speed independent engine drag parameter
beng = Speed dependent engine drag parameter
However, Biggs (1988) noted that the determination of the parameter values for the
engine drag equation was quite problematic with low coefficients of determination and high
standard errors.
Also, Biggs estimates the engine speed as a function of the vehicle speed in order to
compute the engine power. There are two different equations in the engine speed model: One for
a vehicle in top gear; the other for a vehicle in less than top gear. However, these equations lead
to a discontinuous relationship between vehicle speed and engine speed when the vehicle shifts
into top gear. Such discontinuities lead to inconsistent fuel consumption predictions and should
therefore be avoided (Biggs, 1988).
Recently, the World Bank updated the mechanistic fuel consumption model in the HDM-
4 module (Bennett et al, 2003). The model adopted is based on the ARRB ARFCOM
mechanistic model (Australian model) described above, but with a change to the prediction of
engine speed, accessories power, and engine drag. The general form of the model is expressed
conceptually by Equation (A.9).
( ) ( )( )dFuelPtotPengPaccsPtrfIFC +=+= 1**,max, ξα ( A.9)
A-6
where:
IFC = Instantaneous Fuel consumption in mL/s
trP = Power required to overcome traction forces (kW)
accsP = Power required for engine accessories (e.g. fan belt, alternator etc.) (kW)
Peng = Power required to overcome internal engine friction (kW)
α = Fuel consumption at Idling (mL/s)
ξ = Engine efficiency (mL/KW/s)
( )max
1 engtotb
P Pehp
Pξ
− = +
ξb = Engine efficiency depends on the technology type (gasoline versus diesel)
Pmax = Rated engine power
ehp = engine horsepower
dFuel = Excess fuel conception due to congestion
The engine efficiency decreases at high levels of output power, resulting in an increase in
the fuel efficiency factor ξ. The total power required is divided into tractive power, engine drag,
and vehicle accessories, respectively. The total requirement can be calculated by two alternative
methods depending on whether the tractive power is positive or negative as shown in Table A-1.
The tractive power is a function of the aerodynamic, gradient, curvature, rolling resistance and
inertial forces. The aerodynamic forces are expressed as a function of the air density and the
aerodynamic vehicle characteristics and are given in Table A-2. The gradient forces are a
function of vehicle mass, gradient, and gravity. The curvature forces are computed using the slip
energy method. The rolling resistance forces are a function of vehicle characteristics, pavement
conditions, and climate. The inertial forces are a function of the vehicle mass, speed, and
acceleration.
A-7
Table A-1 Current HDM 4 Fuel Consumption Model Name Description Unit
Total power (Ptot) for 0, uphill/level
for 0, downhill
trtot accs eng tr
tot tr accs eng tr
PP P P Pedt
P edtP P P P
= + + ≥
= + + < kW
edt Drive-train efficiency factor
Engine and accessories power (Pengaccs = Peng + accsP ) ( )
RPMIdleRPMRMPIdleRPMaPaccsaPaccsaPaccs
PKPeaPengaccs
−−
−+
=
100*1_0_(1_
max** kW
KPea Calibration factor Pmax Rated engine power kW
Paccs_a1
−==
−=
−+−=
αξ
ξ
cPkPeab
PctPengPkPeaehpa
acabbaPaccs
b
b
max**100
100max****
*2**41_
2
2
ξb Engine efficiency depends on the technology type (gasoline versus diesel)
mL/kW/s
ehp Engine horsepower hp
α Fuel consumption at Idling mL/s
Paccs_a0 Ratio of engine and accessories drag to rated engine power when traveling at 100 km/h
PctPeng Percentage of the engine and accessories power used by the engine (Default = 80%) %
Engine speed (RPM) ( )vSPSPaSPaSPaaRPM
,20max*3*2*10 32
=+++= Rev/min
υ Vehicle speed m/s
a0 to a3 Model parameter (Table A.3) RPM100 Engine speed at 100Km/h Rev/min RPMIdle Idle engine speed Rev/min
Traction power ( trP ) ( )1000tr
Fa Fg Fc Fr FiP
ν + + + += kW
Fa Aerodynamic forces N
Fg Gradient forces N
Fc Curvature forces N
Fr Rolling resistance forces N
Fi Inertial forces N
A-8
Table A-2 Current HDM 4 Traction Forces Model Name Description Unit
Aerodynamic forces (Fa) 2*****5.0 υρ AFCDCDmultFa = N
CD Drag Coefficient CDmult CD multiplier AF Frontal Area m2 ρ Mass density of the air Kg/m3
υ Vehicle speed m/s
Gradient forces (Fg) gGRMFg **= N
M Vehicle weight Kg GR Gradient radians g The gravity M/s2
Curvature forces (Fc)
−
= −3
22
10**
***
,0maxCsNw
egMR
M
Fc
υ
N
R curvature radius m
Superelevation (e) ( )( )RLne *68.045.0,0max −= m/m
Nw Number of wheels
Tire stiffness (Cs)
++=
2
*2*10*NwMa
NwMaaKCSCs
KCS Calibration factor a0 to a2 Model parameter (Table A-4)
Rolling resistance (Fr) ( )( )2*13*12*1*11**2 υbMbCRNwbFCLIMCRFr ++= N
CR1 Rolling resistance tire factor
Rolling resistance parameters (b11, b12, b13)
=
=
=
2/*012.012
/064.0/067.0
12
*3711
DwNwb
tireslatestDwtiresoldDw
b
Dwb
Rolling resistance surface factor (CR2) [ ]DEFaIRIaTdspaaKcr *3*2*102 +++= Kcr2 Calibration factor a0 to a3 Model coefficient (Table A-5) Tdsp Texture depth using sand patch method mm IRI International roughness index m/km DEF Benkelman Beam rebound deflection mm Climatic factor (FCLIM) PCTDWPCTDSFCLIM *002.0*003.01 ++=
Inertial forces (Fi) aaaaMFi *2arctan*10* 3
+=υ
a0 to a2 Model parameter (Table A-6)
A-9
Table A-3 Engine Speed Model Parameters for the Current HDM 4 model (Bennett and Grennwood, 2003)
Vehicle Type
Engine speed
a0 a1 a2 a3
Motorcycle -162 298.86 -4.6723 -0.0026 Small car 1910 -12.311 0.2228 -0.0003 Medium car 1910 -12.311 0.2228 -0.0003 Large car 1910 -12.311 0.2228 -0.0003 Light delivery car 1910 -12.311 0.2228 -0.0003 light goods vehicle 2035 -20.036 0.356 -0.0009 four wheel drive 2035 -20.036 0.356 -0.0009 light truck 2035 -20.036 0.356 -0.0009 medium truck 1926 -32.352 0.7403 -0.0027 heavy truck 1905 -12.988 0.2494 -0.0004 articulated truck 1900 -10.178 0.1521 0.00004 mini bus 1910 -12.311 0.2228 -0.0003 light bus 2035 -20.036 0.356 -0.0009 medium bus 1926 -32.352 0.7403 -0.0027 heavy bus 1926 -32.352 0.7403 -0.0027 Coach 1926 -32.352 0.7403 -0.0027
Table A-4 Cs Model Parameters for the Current HDM 4 model (Bennett and Grennwood, 2003)
coefficient <=2500kg >2500Kg
Bias radial bias radial a0 30 43 8.8 0 a1 0 0 0.088 0.0913 a2 0 0 -0.0000225 -0.0000114
Kcs 1 1 1 1
A-10
Table A-5 Parameters for CR2 Model in the Current HDM 4 Model (Bennett and Grennwood,
2003)
Surface class surface type
<=2500kg >2500Kg a0 a1 a2 a3 a0 a1 a2 a3
Bituminous AM or ST 0.5 0.02 0.1 0 0.57 0.04 0.04 1.34 Concrete JC or GR 0.5 0.02 0.1 0 0.57 0.04 0.04 0 unsealed GR 1 0 0.075 0 1 0 0.075 0 unsealed - 0.8 0 0.1 0 0.8 0 0.1 0
block CB, BR or SS 2 0 0 0 2 0 0 0 unsealed SA 7.5 0 0 0 7.5 0 0 0
Table A-6 Effective Mass Ratio Model Parameters for the Current HDM 4 model (Bennett and
Grennwood, 2003)
Vehicle Type Effect Mass ratio Model Coefficients
a0 a1 a2 Motorcycle 1.1 0 0 Small car 1.14 1.01 399 Medium car 1.05 0.213 1260.7 Large car 1.05 0.213 1260.7 Light delivery car 1.1 0.891 244.2 light goods vehicle 1.1 0.891 244.2 four wheel drive 1.1 0.891 244.2 light truck 1.04 0.83 12.4 medium truck 1.04 0.83 12.4 heavy truck 1.07 1.91 10.1 articulated truck 1.07 1.91 10.1 mini bus 1.1 0.891 244.2 light bus 1.1 0.891 244.2 medium bus 1.04 0.83 12.4 heavy bus 1.04 0.83 12.4 coach 1.04 0.83 12.4
A-11
EVALUATION OF THE EXISTING VOC MODELS
This section evaluates the existing models. The most recent fuel consumption model
(HDM 4) have been implemented in EXCEL spreadsheets in order to study the sensitivity of the
various input variables and to compare their predictions with the results from the U.S. empirical
models (Zaniewski et al., 1982). In general, the model evaluation and selection was based on a
set of criteria that encompasses two distinctive and equally important aspects: (1) practicality of
the model and (2) statistical soundness.
Practicality of the Model
1- Ease of use and availability of appropriate input data: the HDM 4 model is more complicated
than an empirical/regression model because it needs more input variables. However, it is
more flexible than the empirical ones because of its nature, i.e. mechanistic. The input
variables are available from different sources of data. Appendix A2 summarizes the source
for the different input data along with a summary distribution.
2- The ability of the model to incorporate pavement surface conditions as currently being
measured: The input variables related to pavement surface conditions are not as currently
being measured. In fact, the rolling resistance model uses:
• Benkelman beam deflection, and not the FWD deflection to take into account
pavement response. These two measurements can be correlated (for example, see
Ullidtz, 1989); and
• Sand patch method to measure surface texture depth (Tsdp). Currently, DOTs uses
laser -based pavement surface texture measurement devices, which measure the Mean
Profile Depth (MPD) of the pavement. The relationship between these two
measurements is 1.02* 0.28Tsdp MPD= + .
3- Reasonableness and applicability to U.S. conditions: From literature review, it became
apparent that most of the existing models are derived from previous ones; each model aims to
correct a problem found in a previous one. Some major points and/or noteworthy corrections
follow:
- The South African model assumes that the fuel efficiency of the vehicle is independent
from the driving mode. However, Biggs found that the fuel efficiency increases in the
acceleration case (Biggs, 1987). An improved mechanistic model was then developed.
A-12
- In the ARFCOM model, there are two different equations in the engine speed model:
One for a vehicle in top gear; the other for a vehicle in less than top gear. However,
these equations lead to a discontinuous relationship between vehicle speed and engine
speed when the vehicle shifts into top gear. Such discontinuities lead to inconsistent
fuel consumption predictions (Biggs, 1988). In HDM 4, a new regression equation
relating vehicle speed to the engine speed was developed.
- Even though the Swedish model is purely mechanistic, it was noted that it gives
unreasonably high results compared with the real field data. Thus, Hammarström and
Henriksson (1994) calibrated the HDM III to Swedish conditions.
Considering the above point, the research team selected the HDM 4 model for further
evaluation and discussion. In order to evaluate the reasonableness and applicability to U.S.
conditions, the research team compared the empirical (Zaniewski et al.) models to the newer
mechanistic (HDM 4) models. To do so, the research team, first, had to update the Zaniewski
tables using the national average fuel consumption data collected from the Bureau of
Transportation Databases. Figure A.1 shows the evolution of fuel consumption for passenger
cars from 1960 through 2004. The research team followed the same procedure as the one used by
Zaniewski to update the Winfrey tables. Second, the research team applied US conditions, i.e.,
car make and models, tires, and weather conditions and ranges for roughness and texture depths
(collected from the LTPP databases and included in Appendix A.2).
Figure A.2 shows the updated Zaniewski model and HDM IV with US conditions.
Although the shapes of the curves are similar, there are some differences relative to HDM 4
predictions that should be further evaluated:
- First, at low speeds, unlike the HDM 4 predictions, the Zaniewski tables assume that
small cars consume more than large cars, which is counter-intuitive observation.
- Second, unlike the HDM 4 predictions, the Zaniewski tables assume much lower fuel
consumption at higher speeds.
The research team recognizes that these differences are the result of the calibration of
some model parameters to the condition of other countries.
A-13
Figure A-1 Annual Fuel Consumption per Vehicle (Passenger Cars)
Figure A-2 Comparison between Zaniewski’s et al. and HDM 4 Fuel Consumption Prediction (Passenger Cars)
0
20
40
60
80
100
120
140
160
180
200
1960
1965
1970
1975
1980
1985
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Years
Fuel
Con
sum
ptio
n (m
L/K
m)
0
20
40
60
80
100
120
140
160
180
200
0 20 40 60 80 100 120
Speed (Km/h)
Fuel
Con
sum
ptio
n (m
L/K
m)
large car (updated zaniewski) large car HDM IVmedium car (updated zaniewski) medium car HDM IVsmall car (updated zaniewski) small car HDM IV
A-14
Statistical Soundness of the Models
Most of the empirical models were estimated making use of classical regression
assumptions: normality, independence, constant variance. However, it is known that many of the
response variables (VOCs) do not follow the classical assumptions. Also, these models and their
estimates are generally not transferable outside the economical, technological, fleet operating,
and regulatory conditions under which they were developed (Bein, 1993).
Given their nature, the mechanistic models are theoretically formulated so that they
encompass the main physical parameters according to basic laws of physics/mechanics.
Therefore, the assumptions and the formulation should be valid and reasonably accurate. Also,
one can introduce a calibration factor to consider the effect of emerging vehicle technologies on
fuel consumption, which makes the model more flexible for future predictions. This
improvement will result in models that are theoretically sound and more accurate, which directly
translates into more accurate predictions.
A-15
A2 - TYPICAL US CONDITIONS: INPUT DATA SOURCES AND
DISTRIBUTIONS
The research team identified and classified the data elements required for developing the
anticipated models based on their availability level and collection status. The data elements (i.e.
input and output data) are classified within 5 categories:
• Pavement condition,
• Vehicle and tire characteristics,
• Environment,
• traffic, and
• VOC data.
For each category, there are 3 levels of availability:
• Level 1: The data are readily available or could be generated from field tests.
• Level 2: The data are not readily available, but they could be estimated or assumed
as a constant (e.g., averaging a range of published data values).
• Level 3: The data are not readily available and will not be generated as part of the
field tests. In this category, the data will be obtained from other sources if available
or will be set to default values as in HDM-4.
In the following sections, the different input parameters, the VOC data and their
availability are presented.
Tables A-7 and A-8 present the different inputs for the traction forces and their sources of
data. All the input data are readily available (level 1).
The engine and accessories powers are mainly functions of vehicle characteristics. Table
A-9 presents input parameters and their sources of data. All the data are of level 1 availability.
A-16
Table A-7 Input Parameters for Traction Forces Pavement Condition Environment
Vehicle Characteristics Tire Characteristics
Aerodynamic
• Altitude
• Air temp
• Wind speed
• Drag Coefficient
• Frontal Area
Rolling resistance
• Surface type
• Texture depth
• IRI
• Deflection
• % driving – snow
• % driving – rain
• Vehicle mass
• Vehicle speed
• No. of wheels
• Tire type
• Wheel diameter.
Curvature • Curvature radius
• Vehicle mass
• Vehicle speed
• No. of wheels
• Tire type
Gradient • Gradient • Vehicle mass
Inertial
• Vehicle type
• Vehicle mass
• Vehicle speed
• Vehicle acceleration
A-17
Table A-8 Data Sources for Input Parameters for Traction Forces Input parameters Data Sources Method Status
Pavement Condition
• Surface type • Texture depth • IRI, SV • Gradient • Curvature radius • Deflection (FWD)
• Field trials • Selection matrix • Test measurement • Records
• Selection of test sites completed
• Basic summary condition data and records collected
• Detailed test measurements to be collected during field trials
Environment
• Altitude • Air temp • Wind speed • % driving – snow • % driving – rain
• Weather station • National Climatic Data
Center
• Records
Collected
Vehicle Characteristics
• Vehicle Type, mass • Vehicle Speed,
acceleration • Drag Coefficient
Frontal Area • No. of wheels
• Vehicle manufacturers • Selection matrix Collected
Tire Characteristics • Tire type • Wheel diameter
• Tire manufacturers • Selection matrix Collected
A-18
Table A-9 Input Parameters and Data Sources for Engine & Accessories Power Vehicle Characteristics Data sources Status
• Vehicle Type
• Vehicle speed
• Selection matrix Collected
• Rated engine power
• Engine efficiency
• Vehicle manufacturer Collected
• Idle fuel consumption • Field trials collected
Figures A-3 through A-9 show typical input data for pavement condition, environment
(temperature) and vehicle characteristics in the US. Figures A-3(a) through (d) show the
distributions of pavement types in the US. It can be seen that 65% of the roads in the US are
paved. Among the paved roads, 57% are flexible, 6% are rigid, 11% are composite and 26% are
thin surfaced pavements (source: FHWA). IRI data for all states as of 2006 have been extracted
from the FHWA website.
Figures A-4 (a) through (c) show the distributions of IRI of Interstate, US and state
highways in selected states. These data support the distribution of pavement sections by
pavement type and roughness in the proposed experimental matrix for the field trials presented in
chapter 3.
Figure A-5 (a) shows the average monthly air temperatures (2007) for representative
states above and below the national average, while Figure A-5 (b) shows the data for Michigan
conditions. These data was used to correct for the environmental conditions during the field trials
for fuel consumptions.
Vehicle aerodynamic characteristics for all classes in the US have been collected (source:
EPA report, 2007). Figures A-6 (a) and (b) show typical ranges of these characteristics for
passenger cars. Figures A-6 (c) and (d) show these characteristics for specific car models of
popular US and imported brands. These data was used for inputting the specific data for the
vehicles used in the field trials. Figure A-7 reports vehicle weight statistics in the U.S. for trucks
and passenger cars.
A-19
(a) Percent of Mileage by Pavement
Category
(b) Percent of Mileage by Surface Type (Rural
Roads)
(c) Percent of Mileage by Surface Type
(Urban Roads)
(d) Percent of Mileage by Surface Type (All
Roads)
1 mm = 0.04 in
(d) Mean Proficle Depth (All Roads)
Figure A-3 Latest Pavement Type and Mean Profile Depth Frequency Distribution (source: HPMS Database)
0
10
20
30
40
50
60
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Dis
trib
utio
n
Mean Profile Deapth (mm)
A-20
(a) Interstate Highways
(b) US and State Highways
(c) Others
1 m/km = 63.4 in/mile Figure A-4 Latest Pavement Roughness Frequency Distribution (source: HPMS database)
-
20
40
60
80
100
0 1 2 3 4 5 6
% m
ileag
e
IRI (m/km)
-
20
40
60
80
100
0 1 2 3 4 5 6
% m
ileag
e
IRI (m/km)
-
20
40
60
80
100
0 1 2 3 4 5 6
% m
ileag
e
IRI (m/km)
Michigan Texas California Pennsylvania
A-21
(a) Temperature Distribution in Different States
(b) Temperature Distribution in Michigan ( 9 * 325
F C° = ° + )
Figure A-5 Environment Conditions in the US for 2007 (source: National Climatic Data Center))
A-22
(a) Drag Coefficient Ranges by Vehicle Class
(b) Frontal Area Ranges by Vehicle Class
(c) Frontal Area and Drag Coefficient for
Chevrolet
(d) Frontal Area and Drag Coefficient for Toyota
Figure A-6 Latest Aerodynamic Parameters in the US (sources: EPA and CarTest software)
0.46 0.49
0.3 0.3
0.52
0.33
0
0.1
0.2
0.3
0.4
0.5
0.6
Small car Medium car Large car
Dra
g c
oeff
icie
nt
Passenger car categories
1.86
1.76
1.59
2.16
1.95
1.83
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
Small Medium Large
Passenger car categories
Fron
tal a
rea
(m2)
A-23
(a) Percentage of Truck by Truck Weight Class in metric tons
(b) Average Car Weight by Class for 2004-2006 in metric tons
(c) Percentage of Truck by Truck Weight Class in lb
(d) Average Car Weight by Class for 2004-2006 in lb
Figure A-7 Vehicle Weight Statistics in the US Grouped by EPA Vehicle Classification (source: FHWA)
Fuel efficiency data for all vehicles in the US market are readily available from
manufacturers and other sources. Figures A.8 and A.9 shows distributions of these engine
performance parameters for passenger cars and trucks respectively (source: EPA report, 2007).
Specific fuel efficiency values for the vehicles used in the field trials are extracted from the
vehicle manufacturer catalogs. The rated engine power for any vehicle can be determined using
the relationship shown in Figure A-10 (source: EPA report, 2007).
0
10
20
30
40
50
60
70
2.72 4.54 6.35 7.26 8.85 11.79 14.97 More
Perc
enta
ge o
f Tru
cks (
%)
Truck Weight (metric tons)
0
0.5
1
1.5
2
2004 2005 2006
Ave
rage
Car
Wei
ght
(met
ric
tons
)
Year
Small Medium Large
0
10
20
30
40
50
60
70
6,000 10,000 14,000 16,000 19,500 26,000 33,000 More
Perc
enta
ge o
f tru
cks (
%)
Truck Weight (lb)
0
1,000
2,000
3,000
4,000
2004 2005 2006Ave
rage
Car
Wei
ght (
lb)
Year
Small Medium Large
A-24
(a) Average Distribution of Fuel Economy for Passenger Car in the US
(b) Average Distribution of Fuel Efficiency for Passenger Car in the US
Figure A-8 Latest Fuel Economy and Efficiency Distribution in the US for Passenger (source: Consumer Report, 2007)
A-25
(a) Average Distribution of Fuel Economy for Trucks in the US
(b) Average Distribution of Fuel Efficiency for Trucks in the US
Figure A-9 Latest Fuel Economy and Efficiency Distribution in the US for Trucks (source: Consumer Report, 2007)
A-26
Figure A-10 Relationship between Engine Efficiency and Rated Power (source: EPA Report, 2007)
The data from bureau of census database (VITRIS, 2005) was also collected. The
database contains detailed information on trucks in US fleets (Truck use, body type, vehicle size,
annual miles of travel, age, vehicle acquisition, truck type, range of operation, and fuel type,
etc.). Figure A-11 shows an example of such data taken from this source. Figure A-11a shows
annual fuel consumption by vehicle class. It can be seen from the figure that passenger cars,
single unit trucks (SUT), heavy trucks and buses consume about 45, 33, 21 and 1 percent of the
total fuel consumed, respectively. Figure A-11b presents the percent of vehicle-miles traveled by
vehicle class: The data shows similar trends as fuel consumption. Trucks and buses have the
highest fuel consumption as compared to cars and SUT (see Figure A-11c). Although trucks
showed a smaller percent of the total fuel consumed and mileage traveled, they have the highest
annual traveled mileage by vehicle class (see Figure A-11d). Furthermore, time series data for
average mileage per gallon by vehicle class indicates that there is no significant change from
1995 to 2002. However, with increasing fuel costs and demands, it is anticipated that this trend
(in Figure A-11e) will not remain the same. This simple example indicates that the interaction
between various VOC-related factors needs to be considered.
0
5
10
15
20
25
30
0 20 40 60 80 100
Percentage of rated engine power
Effe
cien
cy (%
)
A-27
(a) Annual fuel consumption by vehicle class
(b) Average vehicle miles by vehicle class
(c) Average miles per gallon by vehicle class
(d) Average annual miles by vehicle class
(e) Average miles per gallon by vehicle class, time series Figure A-11 Vehicle Mileage by Vehicle Class for the US (Source: US Census)
Avg. miles per gallon (Year 2002)
0
5
10
15
20
25
Cars Buses Single UnitTruck
Trucks
Avg. miles per vehicle (x 1000)
0
10
20
30
Cars Buses Single UnitTruck
Trucks
APPENDIX B
TIRE WEAR MODELS
B-1
B1 - IDENTIFICATION AND EVALUATION OF TIRE WEAR
MODELS
This appendix summarizes the detailed equations and relationships of current tire wear
models. These models were also evaluated regarding their applicability to the paved surfaces and
traffic and environmental conditions encountered in the United States that are capable of
addressing the full range of vehicle types.
EXISTING VOC MODELS
Although tire consumption is a significant component of the total VOC, especially for
heavy vehicles, unlike fuel consumption, it has received much less attention. For example, it was
found in New Zealand that tire costs constitute about 18% of the VOC for heavy trucks,
compared to only 5% for passenger cars (Bennett and Greenwood, 2003). Cost associated with
tire wear has been affected by changes in tire design and technology in the tire manufacturing.
Radial design and belted construction have increased the mileage life of tires, but increased
prices have offset these gains to some extent (Zaniewski et al. 1982). There are two types of
models which have been developed for predicting tire consumption: (1) empirical, which can be
developed from fleet survey data, and (2) mechanistic, which relate tire consumption to the
fundamental equations of motion and are developed from controlled experiments. This section
briefly reviews some of the major tire consumption models that have been developed.
Empirical Models
Winfrey developed tables for calculating tire wear cost per mile. The tire wear cost was a
function of vertical and horizontal curves, and speed changes (Winfrey, 1969).
In 1973, the U.S. Forest Service funded a project to develop tire wear predictions (based
on the slip energy concept) from measurable tire/road interactions for use in a VOC model for
the national forest service road system. Zaniewski et al. (1982) developed a new model based on
the slip energy concept to calculate tire wear and then present the results in tabular format. The
most current models follow a mechanistic modeling approach to develop tread wear models.
B-2
The Saskatchewan Department of Highways and Transportation (SHT), Canada, adopted
the following tire wear model.
t t
t tt tr
C NTCL K K
= ( B.1)
where:
tC = Cost per tire, $/tire
tN = Number of tires
trK = Road roughness coefficient
ttK = Road texture coefficient
tL = Life of tire (km) As shown in Equation (B.1), the tire costs are a function of tire type, tire quality, road
conditions, and tire maintenance practices. The effect of road surface on tire cost is primarily a
function of road surface texture and roughness.
Mechanistic-Empirical Models
In the US, the tire wear model was developed by relating the volume of tread rubber
worn to the amount of slip energy expanded at the pavement-tire interface. Equation (B.2) shows
the form of the model:
/WR SLIP WEV E S= ( B.2)
where:
VWR = Volume of worn tread rubber, in3 (1 in = 25.4 mm)
ESLIP = Slip energy, lb-mi (1 lb-mi = 7,159 N.m)
SWE = Slip energy-volume wear coefficient, (lb-mi)/in3
In this model two coefficients must be experimentally determined to be representative of
specific tire and pavement surface types; these are slip and energy-volume wear coefficients.
The World Bank HDM 3 model adopted the slip energy model to calculate the changes in
tread wear as shown below:
0TWT K NFTµ λ∆ = × × × ( B.3)
B-3
where:
TWT∆ = Change in tread wear 0K = Calibration factor reflecting pavement properties
µ = Coefficient of friction NFT = Normal force on the tire (N) λ = Tire slip
For HDM 4, the model has been extended to include horizontal curvature force and
traffic interaction effects, as shown below (Bennett and Greenwood, 2003).
The general form of the tire consumption model is the following:
MODFACEQNTNWTC *
= ( B.4)
where: NW = Number of wheels EQNT = Number of equivalent new tires consumed per 1000 km MODFAC = Tire life modification factor
Table B.1 presents the details of this model. Carpenter and Cenek (2000) noted that, when
testing the model, the values for C0tc were found to be too low and resulted in an unreasonably
high tire life. Therefore, due to the problems with this model, an interim model was adopted for
HDM 4. In fact, a constant was added to the EQNT equation in Table B-1, which becomes as
follows:
0027.0**1*1
+
++
=VOLTWT
NRRTWRNRRRECEQNT ( B.5)
The tire life modification factors were proposed by Harrison and Aziz (1998). They
depend on the roughness, tire type and congestion level and are calculated using the equation
described in Table B-1.
B-4
Table B-1 HDM 4 Tire Consumption Model Name Description Unit
Number of equivalent new tires (EQNT) VOL
TWTNRRTWRNRRRECEQNT *
*1*1
++
= 1/1000 km
RREC = The ratio of the cost of retreads to new tires RTWR = The life of a retreaded tire relative to a new tire VOL = Tire volume dm3
Number of retreading (NR) ( )( )0.03224* Immax 0, 0* 1R odNR NR e −= −
NR0 = The base number of retreads for very smooth, tangent roads (Table B.2)
RImod = Model Parameter (Table B.2)
Total change in tread wear (TWT)
2 2
0
0
tc tcte
tc tcte
CFT LFTTWT C CNFT
TWT C C TE
+= + ×
= + × dm3/1000 km
C0tc = The tread wear rate constant (Table B.2) dm3/1000 km Ctcte = The tread wear coefficient (Table B.2) dm3/MNm
The tire energy (TE) 2 2CFT LFTTENFT+
= MNm/1000 km
The circumferential force on the tire (CFT)
( ) ( )1 * *CTCON dFUEL Fa Fr FgCFT
NW+ + +
= N
CTCON = The incremental change of tire consumption related to congestion.
dFUEL = The incremental change of fuel consumption related to congestion
Fa = The aerodynamic forces N Fr = The rolling resistance forces N Fg = The gradient forces N The lateral force on the tire (LFT)
FcLFTNW
= N
Fc = The curvature forces N NW = Number of wheels The normal force on the tire (NFT)
*M gNFTNW
= N
M = Vehicle mass kg g = Gravity m/sec2 Tire life medication factor (MODFAC)
CONFACTYREFACVEHFACMODFAC **=
VEHFAC = A vehicle specific modification factor (Table B.2) TYREFAC = A tire type modification factor (see Table B.3) Congestion modification factor (CONFAC)
≥<
=85.00.185.07.0
VCRVCR
CONGFAC
B-5
Table B-2 Tread Wear Rate Constants (Bennett and Greenwood, 2003)
Vehicle type C0tc
(dm3/1000 km) Ctcte
(dm3/MNm) RImod NR0 VEHFAC
Motorcycle 0.00639 0.0005 IRI 1.3 2 Small car 0.02616 0.00204 IRI 1.3 2 Medium car 0.02616 0.00204 IRI 1.3 2 Large car 0.02616 0.00204 IRI 1.3 2 Light delivery car 0.024 0.00187 IRI 1.3 2 Light goods vehicle 0.024 0.00187 IRI 1.3 2 Four wheel drive 0.024 0.00187 IRI 1.3 2 Light truck 0.024 0.00187 IRI 1.3 2 Medium truck 0.02585 0.00201 min(7,IRI) 1.3 1 Heavy truck 0.03529 0.00275 7 1.3 1 Articulated truck 0.03988 0.00311 min(7,IRI) 1.3 1 Mini bus 0.024 0.00187 IRI 1.3 2 Light bus 0.02173 0.00169 IRI 1.3 2 Medium bus 0.02663 0.00207 7 1.3 1 Heavy bus 0.03088 0.00241 min(7,IRI) 1.3 1 Coach 0.03088 0.00241 min(7,IRI) 1.3 1
Table B-3 Tire Type Modification Factor (Harrison and Aziz (1998))
Tire Type Paved Roads
Unpaved Roads IRI<=6 m/Km IRI>6 m/Km
Bias 1 1 1 Radial 1.25 1.2 1
EVALUATION OF THE EXISTING VOC MODELS
This section evaluates the existing models. The most recent tire wear model (HDM 4) has
been implemented in EXCEL spreadsheets in order to study the sensitivity of the various input
variables and to compare their predictions with the results from the U.S. empirical models
(Zaniewski et al.).
Practicality of the Model
1- Ease of use and availability of appropriate input data: The HDM 4 model is more
B-6
complicated than the empirical/regression models because it needs more input variables.
However, it is more flexible than the empirical ones given its nature, i.e., mechanistic. The
input variables are available from different sources of data, e.g., tire manufacturers.
Appendices A2 and B2 summarize the source for the different input data along with a
summary distribution.
2- The ability of the model to incorporate pavement surface conditions as currently being
measured: The input variables related to pavement surface conditions (e.g., roughness) are
not necessarily compatible with what is currently being measured/used in the U.S. In fact,
the rolling resistance model uses:
• Benkelman beam deflection, and not the FWD deflection to take into account
pavement response. These two measurements can be correlated (for example, see
Ullidtz, 1989); and
• Sand patch method to measure surface texture depth (Tsdp). Currently, DOTs uses
laser -based pavement surface texture measurement devices, which measure the Mean
Profile Depth (MPD) of the pavement. The relationship between these two
measurements is 1.02* 0.28Tsdp MPD= + .
3- Reasonableness and applicability to US conditions: Similarly to fuel consumption, the
research team applied US conditions to the HDM 4 model and compared it to the Zaniewski
model. Figure B-1 shows many similarities between the two models. In fact, the curves have
the same shape. The results from both models are in agreement, i.e., large cars consume more
tread than medium cars which consume more than small cars.
Figure B-2 shows the interaction between roughness and tire consumption. Roughness
appears to have a major effect on tire consumption which is in agreement with the literature
review. Even though Figures B-1 and B-2 show the results for only passenger cars, similar
results were found for heavy vehicles. However, because of large differences in scale, results
for passenger cars and heavy vehicles could not be displayed on the same graph. Figure B-3
shows the effect of roughness on truck tire consumption.
B-7
Figure B-1 Comparison between Zaniewski and HDM 4 Models (Tire Wear)
Figure B-2 Effect of Roughness on Tire Consumption for Passenger Cars (HDM 4)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 20 40 60 80 100 120
Speed(Km/h)
Tire
con
sum
ptio
n (%
wea
r/100
0km
)
small car zaniewski medium car zaniewski large car zaniewskismall car HDM IV medium car HDM IV large car HDM IV
0
0.1
0.2
0.3
0.4
0.5
0 1 2 3 4 5
IRI(m/Km)
Tire
Con
sum
ptio
n (%
wea
r/100
0 K
m)
small car (zaniewzki) medium car (zaniewski) large car (zaniewski)small car (HDM IV) medium car (HDM IV) large car (HDM IV)
B-8
Figure B-3 Effect of Roughness on Tire Consumption for Trucks (HDM 4)
Statistical Soundness of the Models
While empirical models and their estimates are generally not transferable outside the
economical, technological, fleet operating, and regulatory conditions under which they were
developed (Bein, 1993), the assumptions and the formulation of mechanistic models are valid
and reasonably accurate. Therefore, mechanistic models are theoretically sound and more
accurate, which directly translates into more accurate predictions.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 1 2 3 4 5
IRI (m/Km)
Tire
con
sum
ptio
n (%
wea
r/100
0km
)
SUV Light TruckV Articulated Truck
B-9
B2 - TYPICAL US CONDITIONS: INPUT DATA SOURCES AND
DISTRIBUTIONS FOR TIRE WEAR DATA
Tables B-4 and B-5 present the different inputs for the traction forces in the HDM 4 and their
sources of data. All the input data are readily available (same as fuel consumption). Possible
exceptions are the tire wear rate coefficients, tcteC and otcC . The values for otcC used in HDM 4
were arrived at by using estimated tire lives. The tread wears were calculated using typical
pavement conditions and tire lives proposed by Cenek and Carpenter (HDM 4, 2000). A further
review of the literature revealed more information on these rates. Reported tire wear rate results
include (Kennedy et al, 2002):
• Environment Agency (1998) summarized passenger car tire wear rate information in the United Kingdom. Using a life of tire weight loss of 1.5 kg over 50,000 km estimated wear per km corresponds to 30 mg/km/tire or (0.03 dm3/1000 km). This corresponds to a wear rate of 120 mg/VKT under average driving conditions. (VKT = Vehicle Kilometers Traveled)
• Muschak (1990) estimated that motor vehicles lost 120 mg/VKT in Germany.
• New Zealand Ministry of Transportation (1996) estimated that 1,850 g of rubber was lost from a tire during the time that it took to be replaced. This amount to 53 mg/km (0.053 dm3/1000 km) per tire if a lifetime of 35,000 km is assumed.
• Legret & Pagotto (1999) identified an average wear rate for a single tire as 17 mg/km (0.017 dm3/1000 km). This was based upon the following information: 50,000 km wear, dimensions 0.54 m in diameter, 0.12 m wide, 6 mm thickness, density of 1 g/cm3, 30% void due to tread depth, total material loss 857 g. The authors assumed that the amount for heavy vehicles (>3.5 tons is twice this quantity).
• Le Maitre et al. (1998) reported tire wear rates for passenger vehicles from tire/driver surveys of wear rates. They reported wear rates for passenger cars under a range of route/driving conditions. For example:
Highway use: 0.5 g/100 km or 5 mg/km (0.0005 dm3/1000 km).
Urban use: 2.8 g/100 km or 28 mg/km (0.028 dm3/1000 km).
Medium use: 3.3 g/100 km or 33 mg/km (0.033 dm3/1000 km).
Severe use: 7.8 g/100 km or 78 mg/km (0.078 dm3/1000 km).
B-10
Table B-4 Input Parameters for Tire Consumption Model
Pavement Condition Environment/ Traffic Vehicle Characteristics Tire Characteristics
Equivalent new tire Number of retreadings
• IRI • Texture • Curvature • Gradient
• Tire volume • Ratio of cost of retreads
to new tires • Life of retreated tire
relative to new tire Tire life modification factor
• Pavement type • Volume to capacity ratio
• Vehicle type • Tire type
Traction forces Same as Fuel Consumption
Table B-5 Data Sources for Input Parameters of the Tire Consumption Model Input parameters Data Sources Method Status
Pavement Condition
• Surface type • IRI • Texture • Curvature • Gradient
• Field trials, NCAT • HPMS
• Selection matrix • Test measurement • Records
• Selection of test sites completed
• Basic summary condition data and records collected
• Detailed test measurements to be collected during field trials
Environment/ Traffic • Same as Fuel consumption / • Volume to capacity ratio
• SHA • Records Collected
Vehicle Characteristics • Vehicle Type • Known • Selection matrix Collected
Tire Characteristics
• Tire type • Tire volume • Ratio of cost of retreads to
new tires = 50% • Life of retreated tire relative
to new tire = 75%
• Tire manufacturers
• Information Bureau of Retreading
• Selection matrix
Collected
B-11
• Kennedy et al (2002) reported the following wear rates for New Zealand based on industry-supplied measures for material loss and a minimum service life of 35,000 km for passenger cars (PC), light trucks and heavy trucks. Industry- indicators over the tire service life were:
Passenger car tire: 0.031 g/km (0.031 dm3/1000 km) Light truck tire: 0.051 g/km (0.051 dm3/1000 km) Heavy truck tire: 0.21 g/km (0.21 dm3/1000 km)
Table B-6 provides a summary of the wear rate information based on average life of tire
wear rates described above (Kennedy et al., 2002).
Table B-6 Summary of Average Vehicle Tire Wear Rates Under Average Driving Conditions (Kennedy et al, 2002)
Reference Passenger Car (dm3/1000 km)
Light Truck (dm3/1000 km)
Heavy Truck (dm3/1000 km)
Cadle & Williams (1978) 0.03 Rogge et al. (1993) 0.006-0.09 New Zealand Ministry of Transport (1996)
0.037-0.053
New Zealand Environmental Agency (1998)
0.0355
Le Maitre et al. (1998) 0.033 (medium use) Legret & Pagotto (1999) 0.017 ~0.034 Cenek et al. (1993) and Carpenter and cenek (1999)
0.02 (low severity)
New Zealand industry estimate (Kennedy et al, 2002)
0.022-0.031 0.026-0.035 0.046-0.062
Cenek (1993) measured a value for tcteC of 0.00041 dm3/MNm for the Nissan Pulsar. He
reported that an earlier study by Hodges & Koch (1979) had measured values for tcteC for a car
in the range 0.0003 to 0.0009 dm3/MNm for radial car tires on different road surfaces. The
values for tcteC used in HDM 4 were arrived at by assuming that these lower and upper values of
tcteC to be representative of truck tires and motorcycle tires respectively. The values for the
other vehicle classes are:
• For motorcycles tcteC = 0.0009 dm3/MNm • For all cars, delivery vehicles and 4 wheel drives : tcteC = 0.0005 dm3/MNm • For all trucks and buses: tcteC = 0.0003 dm3/MNm
B-12
The truck tire consumption data as well as pavement surface roughness and texture depth
data were collected from NCAT. These data was used to estimate the tire wear rate and validate
the HDM-4 tire consumption model for trucks. Figure B-4 shows the tire replacement data for
steer and drive-trailer axles for the NCAT test track.
Figure B-4 Tire Consumption Data from the NCAT Test (source: NCAT)
Field trials were conducted to collected tire wear data for passenger cars. Assuming that a
measurable tire wear using the laser scanner (tread depth apparatus accuracy is 4 microns) is
about 0.1 mm and using the tire life information (Table 4.2), the minimum length for road
sections at which the vehicle is driven at constant speed is 1040 km for passenger cars. Figure B-
5 shows the results for the M99 sections. It was observed that the variability in tire wear between
trials was significant. It is believed that, since 0.1 mm is a very small number, it is very sensitive
to measurement error.
Figures B-6 through B-10 show the “before” and “after” tread depth data for each field
trial. The comparison of the “before” and “after” tread depth data within each trial showed that:
- For the left front tire (both positions) and the right rear tire first position: The overall
trend is similar for all field trials except for Trial 3. The trend of the “before” data for
Trial 3 was different than the trend of the “after” data for Trial 2. It is believed that an
error in the laser positioning caused this difference. Therefore, the data collected during
the third trial could not be used.
0102030405060708090
100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 More
Perc
enta
ge (%
)
Tire Wear Rate (mm/1000 km)
Drive or Trailer
Steer
Cumulative - Drive or TrailerCumulative - Steer
B-13
- For the right rear tire second position: The overall trend is similar for all field trials
except for Trial 2. The level of error was too high and an increase in tread depth was
observed. Therefore, the data collected during Trial 2 could not be used.
(a) Left Front Tire --- First Position
(b) Left Front Tire --- Second Position
(c) Right Rear Tire --- First Position
(d) Right Rear Tire --- Second Position
(e) Left Rear Tire --- First Position
(f) Left Rear Tire --- Second Position
Figure B-5 Tire Wear Data Collected for Each Field Trial for the M99 (1040 km)
-0.05
0
0.05
0.1
0.15
0.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 2 Trial 3 Trial 4 Trial 5
-0.2
0
0.2
0.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 2 Trial 3 Trial 4 Trial 5
-0.05
0
0.05
0.1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 2 Trial 3 Trial 4 Trial 5
-0.15
-0.05
0.05
0.15
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell NumberTrial 2 Trial 3 Trial 4 Trial 5
-0.05
0
0.05
0.1
0.15
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 4 Trial 5
-0.05
0
0.05
0.1
0.15
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 4 Trial 5
B-14
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure B-6 Tire Comparison between “before” and “after” tread depth data for each field test – Left front position 1
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 2 After 2
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 2
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 3
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 3
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 4
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
Cell Number
Before 5 After 4
6.8
7
7.2
7.4
7.6
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 5
B-15
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure B-7 Comparison between “before” and “after” tread depth data for each field test – Left front position 2
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 2 After 2
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 2
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 3
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 3
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 4
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 4
8.4
8.6
8.8
9
9.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 5
B-16
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure B-8 Comparison between “before” and “after” tread depth data for each field test – Right Rear position 1
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 2 After 2
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 2
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 3
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
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d D
epth
(mm
)
Cell Number
Before 4 After 3
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 4
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 4
4.4
4.8
5.2
5.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 5
B-17
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure B-9 Comparison between “before” and “after” tread depth data for each field test – Right Rear position 2
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 2 After 2
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 3 After 2
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
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Before 3 After 3
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
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d D
epth
(mm
)
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Before 4 After 3
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
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d D
epth
(mm
)
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Before 4 After 4
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
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d D
epth
(mm
)
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Before 5 After 4
6.2
6.6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 5
B-18
(a) Trial 4 – Position 1
(b) Trial 5 – Position 1
(c) Trial 4 – Position 2
(d) Trial 5 – Position 2
Figure B-10 Comparison between “before” and “after” tread depth data for each field test – Left Rear
6.6
7
7.4
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 4 After 4
6.6
7
7.4
7.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
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d D
epth
(mm
)
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Before 5 After 5
8.2
8.6
9
9.4
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epth
(mm
)
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8.2
8.6
9
9.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Trea
d D
epth
(mm
)
Cell Number
Before 5 After 5
A sensitivity analysis was performed to field-test whether the estimated
minimum distance is sufficient to establish a statistically measurable tire wear. The
cumulative tire wear was first computed for 1040, 2080, 3120 and 4000 km (650,
1300, 1950 and 2500 miles). Then, it was normalized to the same mileage (1000 km),
see Figure B-11. Finally, the coefficient of correlation was computed using: the 4000
km data as the baseline for left front tire (positions 1 and 2) and right rear tire
(position 1); 3120 km data for right rear tire (position 2); and 2080 km data for the left
rear tire (positions 1 and 2). Figure B-12 shows these results. Assuming that a
reasonable level of the coefficient of correlation (ρ) is 0.7, it is concluded that the
vehicle should be driven for at least 3120 km to obtain a measurable tire wear.
According to the Uniform Tire Quality Grading Standards (UTQGS) in 49 CFR
575.104, the vehicle should be driven for at least 2560 km (1600 miles) for measuring
tire wear. Therefore, the results shown in Figure B-12 confirm this recommendation.
Nonetheless, it was decided to extend the field test to 4000 km, and use the data from
4000 km runs where ever possible.
B-19
(a) Left Front tire --- First Position
(b) Left Front tire --- Second Position
(c) Right Rear tire --- First Position
(d) Right Rear tire --- Second Position
(e) Left Rear tire --- First Position
(f) Left Rear tire --- Second Position
Figure B-11 Normalized tire wear percentiles for each possible mileage – M99
0
25
50
75
100
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)
1040 km (Trial 2) 1040 km (Trial 4) 1040 km (Trial 5)2080 km 3120 km 4000 km
0
25
50
75
100
-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)1040 km (Trial 2) 1040 km (Trial 4) 1040 km (Trial 5)2080 km 3120 km 4000 km
0
25
50
75
100
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)
1040 km (Trial 2) 1040 km (Trial 4) 1040 km (Trial 5)2080 km 3120 km 4000 km
0
25
50
75
100
0 0.02 0.04 0.06 0.08 0.1 0.12
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)
1040 km (Trial 3) 1040 km (Trial 4)1040 km (Trial 5) 2080 km (Trial 3-Trial 4)2080 km (Trial 4-Trial 5) 3120 km
0
25
50
75
100
0 0.02 0.04 0.06 0.08
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)
1040 km (Trial 4) 1040 km (Trial 5) 2080 km
0
25
50
75
100
0 0.02 0.04 0.06 0.08 0.1
Perc
entil
es (%
)
Accumulated Tire Wear (mm/1000 km)
1040 km (Trial 4) 1040 km (Trial 5) 2080 km
B-20
Figure B-12 Coefficients of Correlation between Tire Wear Data for 1040, 2080, 3120 and 4000
km (650, 1300, 1950 and 2500 miles)
In cases where the 4000 km wear data is not available, it was deemed acceptable to use
the data from 3120 km runs. The accumulated tire wear is computed as the change in tread depth
from “before” trial 2 to “after” trial 5 (4000 km) for LF both positions and RR tires position 1.
For RR tires position 2, the change in tread depth was computed using the “before” data of Trial
3 and the “after” data of Trial 5 (3120 km). Since the tire wear for the LR tires could only be
measured after 2080 km (using the “before” data of Trial 4 and “after” data of Trial 5), the M99
data for the LR tires were not used. The results are shown in Figure B-13. Figure B-14 shows the
tire wear data collected during the I69 field test. The measurement was performed before the test
start and after 4000 km.
0
0.25
0.5
0.75
1
LF position 1 RR position 1 LR position 1 LF position 2 RR position 2 LR position 2
Coe
ffic
ient
of C
orre
latio
n
1040 km 1040 km 1040 km 2080 km 2080 km 3120 km 4000 km
B-21
(a) Left Front Tire --- First Position
(b) Left Front Tire --- Second Position
(c) Right Rear Tire --- First Position
(d) Right Rear Tire --- Second Position
Figure B-13 Accumulated Tire Wear Data Collected During Field Tests for M99 (2080 km)
0.15
0.2
0.25
0.3
0.35
0.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
0
0.04
0.08
0.12
0.16
0.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
0
0.04
0.08
0.12
0.16
0.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
B-22
(a) Left Front Tire --- First Position
(b) Left Front Tire --- Second Position
(c) Right Rear Tire --- First Position
(d) Right Rear Tire --- Second Position
Figure B-14 Accumulated Tire Wear Data Collected During Field Tests for I69 (4000 km)
0.32
0.34
0.36
0.38
0.4
0.42
0.44
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell NumberTrial 1 Trial 1 - Repeat 1Trial 1 - Repeat 2 Trial 2
0.280.3
0.320.340.360.38
0.40.420.44
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 1 Trial 1 - Repeat 1Trial 1 - Repeat 2 Trial 2
0.05
0.1
0.15
0.2
0.25
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell Number
Trial 1 Trial 1 - Repeat 1Trial 1 - Repeat 2 Trial 2
0.1
0.15
0.2
0.25
0.3
0.35
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Acc
umul
ated
Tir
e Wea
r (m
m)
Cell NumberTrial 1 Trial 1 - Repeat 1Trial 1- Repeat 2 Trial 2
APPENDIX C
REPAIR AND MAINTENANCE MODELS
C-1
C1 - IDENTIFICATION AND EVALUATION OF REPAIR AND
MAINTENANCE MODELS
This appendix summarizes the detailed equations and relationships of current repair and
maintenance models. These models were also evaluated regarding their applicability to the paved
surfaces and traffic and environmental conditions encountered in the United States that are
capable of addressing the full range of vehicle types.
EXISTING VOC MODELS
Vehicle repair/maintenance costs are mainly comprised of two components: Parts
consumption and labor hours. The current models can be grouped into empirical- and
mechanistic-based models. The only available U.S. models are also those of the Texas Research
and Development Foundation (TRDF) developed by Zaniewski et al (1982). The most recent
models have been developed outside the U.S. The relevant models are:
• The World Bank’s HDM 3 and 4 models;
• Saskatchewan models (Canada);
• South African model
• Swedish VETO models.
This section briefly reviews some of the major fuel consumption models (identified by
the research team) that have been developed.
Empirical Models Winfrey (1969) presented maintenance costs based on the results of surveys. These were
updated by Claffey (1971) and Zaniewski et al. (1982) using an approach which has been termed
the constituent component approach. Papagiannakis (1999) provided the results of a study into
heavy truck parts consumption. It was noted that there is a significant increase in the
maintenance costs with vehicle age, and at the same time the percentage of the costs due to labor
decreases. This indicates, not unexpectedly, an increase in the number of parts replaced as the
vehicle ages. The parts consumption was affected by road roughness even at low levels (< 2 IRI
m/km). Also, considering the effect of roughness on vehicle maintenance, SHT, Canada adopted
C-2
a vehicle maintenance cost model that relates maintenance costs to roughness:
cf mrMC M K= ( C.1)
where:
MC = Maintenance cost ($/km) Mcf = Average maintenance cost ($/km) Kmr = Road roughness coefficient
HDM 3 allowed users to predict VOC using relationships derived from road user cost
studies in Brazil, India, Kenya, and the Caribbean. The Brazil relationships were the ‘standard’
relationships in HDM 3. Equations C.2 through C.6 show the Brazilian model.
0 exp( ) for IRI IRI0SP kpPARTS C SP CKM CSPIRI IRI= × × × ≤ ( C.2)
0 1 ( ) for IRI > IRI0SPkpPARTS CKM a a IRI= + × ( C.3)
0 0 exp( 0 )(1 - 0 )a C SP CSPIRI IRI SP CSPIRI IRI SP= × × ( C.4)
1 0 exp( 0 )a C SP CSPIRI CSPIRI IRI SP= × × ( C.5)
( )IRICLHIRIPARTSLHCLH CLHPC exp0= ( C.6)
where:
PARTS = Standardized parts consumption as a fraction of the replacement vehicle price per 1000 km CKM = Vehicle cumulative kilometer and it is calculated as half the lifetime kilometreage a0, a1 = Model parameters kp = Model Constant (Table C-1) IRI = Roughness in IRI m/km IRI0SP = Transitional roughness beyond which the relationship between parts consumption and
roughness is linear C0SP = Parts model constant CSPIRI = Parts model roughness coefficient LH = Number of labor hours per 1000 km C0LH = Labor model constant CLHIRI = Labor model roughness coefficient
The Brazilian model (Equations C.2 through C.6) actually incorporates several vehicle
classes including passenger cars, utility vehicles, buses and trucks. For example, the HDM 3
maintenance model suggests parameters for parts and labor for all the above vehicle classes, as
shown in Table C-1.
C-3
The structure of the parts model as shown above is quite complicated because trucks were
found to have a linear response to roughness while passenger cars, utility vehicles, and buses had
an exponential response. Therefore, a linear relationship was adopted above a certain roughness
level (IRI0SP).
Table C-1 HDM 3 Maintenance Model Parameters (Bennett and Greenwood, 2003)
The Council for Scientific and Industrial Research (CSIR) in South Africa developed
models for parts and labor consumption (du Plessis, 1989). The research can be grouped into two
areas: Speed and roughness effects on parts consumption as well as labor costs. Equations C.7
presents the speed effect on the total cost. Equation C.8 presents the roughness effect on parts
consumption. Equations C.9 and C.10 present the South African labor hours’ relationships. The
South African model includes two separate equations for buses and trucks when modeling labor
costs:
231 2 4 aPCST a a v a v
S= + × + + ×
( C.7)
3 exp(-3.0951 0.4514 ln 1.2935 ln(13 )) 10PARTS CKM IRI= + + × ( C.8)
0.517
0.763 exp (0.0715 ) PARTSLH IRINVPLT
=
for buses ( C.9)
max 3, - 0.375 0.0715 0.182PARTSLH IRINVPLT
= + + for trucks
( C.10)
where:
PCST = Maintenance cost in cents/km
Vehicle Class Parts Model Parameters Labor Model Parameters
kp C0SP (x10-6)
CSPIRIP (x10-3) IRI0SP C0LH CLHPC CLHIRI
Passenger Car 0.308 32.49 178.1 9.2 77.14 0.547 0 Utility 0.308 32.49 178.1 9.2 77.14 0.547 0 Large Bus 0.483 1.77 46.28 14.6 293.44 0.517 0.0715 Light and Medium Truck 0.371 1.49 3273.27 0 242.03 0.519 0 Heavy Truck 0.371 8.61 459.03 0 301.46 0.519 0 Articulated Truck 0.371 13.94 203.45 0 652.51 0.519 0
C-4
v = Speed (km/h) a1 to a4 = Model constants PARTS = Standardized parts consumption as a fraction of the replacement vehicle price per 1000 km CKM = Vehicle cumulative kilometer and it is calculated as half the lifetime kilometrage IRI = Roughness in IRI m/km LH = Labor hours per 1000 km NVPLT = the replacement vehicle price less tires
The key problem with this model is that it is sensitive to the assumed average speed. In
fact, du Plessis (1989) proved that there is a huge difference in the parts cost when assuming
urban versus rural speed.
For HDM 4, the parts model was simplified over that used in HDM 3 (Bennett and
Greenwood 2000). Equations C.11 through C.14 show the final model.
( ) ( )kppc 0 1 pcPARTS = K0 CKM (a + a RI) + K1 1 + CPCON dFUEL × ( C.11)
( )( )42 3max ,min 0, * aRI IRI IRI a a IRI= + ( C.12)
5
2 5
53 0
45
5
0
00
0 3
IRIa
a IRI aaa
IRIIRIaa
a IRI
= −
=
=
= −
( C.13)
( )7alh 6 lhLH =K0 a PARTS + K1× ( C.14)
where:
PARTS = Standardized parts consumption as a fraction of the replacement vehicle price per 1000 km K0pc = Rotational calibration factor (default = 1.0) CKM = Vehicle cumulative kilometer (Table C-2) a0, a1, kp = Model Constants (Table C-2) RI = Adjusted roughness IRI = Roughness in IRI m/km IRI0 = Limiting roughness for parts consumption in IRI m/km (3 m/km) a2 to a5 = Model parameters K1pc = Translational calibration factor (default = 0.0) CPCON = Congestion elasticity factor (default = 0.1)
C-5
dFUEL = Additional fuel consumption due to congestion as a decimal LH = Number of labor hours per 1000 km K0lh = Rotation calibration factor (default = 1) K1lh = Translation calibration factor (default = 0) a6 , a7 == Model Constants (Table C-2)
HDM 4 repair and maintenance model suggests parameters for parts and labor for all the
above vehicle classes, as shown in Table C-2.
Table C-2 HDM 4 Maintenance Model Parameters
Vehicle Type Parts consumption model Labor Model
CKM (km) kp a0*1E-6 a1*1E-6 a6 a7
Motorcycle 50,000 0.308 9.23 6.2 1161.42 0.584 Small car 150,000 0.308 36.94 6.2 1161.42 0.584 Medium car 150,000 0.308 36.94 6.2 1161.42 0.584 Large car 150,000 0.308 36.94 6.2 1161.42 0.584 Light delivery car 200,000 0.308 36.94 6.2 611.75 0.445 Light goods vehicle 200,000 0.308 36.94 6.2 611.75 0.445 Four wheel drive 200,000 0.371 7.29 2.96 611.75 0.445 Light truck 200,000 0.371 7.29 2.96 2462.22 0.654 Medium truck 240,000 0.371 11.58 2.96 2462.22 0.654 Heavy truck 602,000 0.371 11.58 2.96 2462.22 0.654 Articulated truck 602,000 0.371 13.58 2.96 2462.22 0.654 Mini bus 120,000 0.308 36.76 6.2 611.75 0.445 Light bus 136,000 0.371 10.14 1.97 637.12 0.473 Medium bus 245,000 0.483 0.57 0.49 637.12 0.473 Heavy bus 420,000 0.483 0.65 0.46 637.12 0.473 Coach 420,000 0.483 0.64 0.46 637.12 0.473
The model suggests eliminating the effects of roughness on parts consumption at low IRI.
This is achieved by using Equations C.12 and C.13. However, Papagiannakis (2000) reported
that even low magnitude roughness has an effect on parts consumption (see Table C-3 below).
C-6
Table C-3 Effect of Operating Conditions on VOC Components (Papagiannakis 2000)
VOC Component Effect on VOC Component Geometry Roughness Capacity
Fuel • Tire - • Oil - • - Parts - • Labor - • Depreciation and Interest • Crew • Passenger Time •
Index: - No effect • Minor effect Major effect
Mechanistic Models The only purely mechanistic model is the VETO model which was developed by the
Swedish Road and Traffic Research Institute (VTI) (Hammarström and Karlsson, 1991). It
contains two approaches to the calculation of parts and maintenance labor: one empirical and one
mechanistic. The former relies on the HDM Brazil relationships (Equations C..2 to C.6) while
the latter employs a "wear index" for vehicle components. The mechanistic model is a detailed
simulation of an idealized two-dimensional vehicle traveling over a surface with a specified
profile. The model works on the basis that the wear and tear of components depends upon the
product of the number of stress cycles they have been subjected to and the stress amplitude
raised to the sixth power. The number of cycles is assumed to be constant per unit length of road
(independent of roughness) while the stress amplitude for each component is proportional to the
RMS value of the dynamic component of the wheel load. The model does not take into account
the static load. The model was calibrated by looking at the life expectancy of different
components. Only four components were studied and so the model does not yet provide a total
cost calculation. Nevertheless, it is interesting to note that the change in vehicle wear with
increasing roughness that it calculates is far higher than the change in parts cost predicted by the
empirical model. In spite of developing a complex model, Hammarström and Karlsson
(Hammarström, 1994) concluded that:
"… it would probably be virtually impossible to develop a model which could be used for
calculating the relationship between total repair costs and road unevenness, component by
component."
C-7
Hammarström and Henrikson (1994) produced coefficients for calibrating HDM 3 to
Swedish conditions. The study produced scaling constants (C0SP) and was also able to examine
how parts consumption changes with vehicle age (kp). However, it did not provide information
on the effect of roughness on parts consumption. Now, the Swedish Road and Traffic Research
Institute are trying to update the HDM 4 model to Sweden condition (Hammarstrom, 1994).
EVALUATION OF THE EXISTING VOC MODELS
This section evaluates and recommends the existing models The most recent repair and
maintenance model (HDM 4) have been implemented in EXCEL spreadsheets in order to study
the sensitivity of the various input variables and to compare their predictions with the results
from the U.S. empirical models (Zaniewski et al.). In general, the model evaluation and selection
was based on a set of criteria that encompasses two distinctive and equally important aspects: (1)
practicality of the model and (2) statistical soundness.
Practicality/ Statistical Soundness of the Model From the literature review, it became clear that most of the models are derived from
previous ones. Each model attempts to address a problem found in a previous one. Some major
points and/or corrections that are noteworthy follow:
– As shown in the previous section, the structure of the Brazilian parts consumption
model is quite complicated because trucks were found to have a linear response to
roughness while passenger cars, utility vehicles and buses had an exponential
response. Also, it requires many input variables that are not easy to compute;
– The key problem with the South African model is that it is sensitive to the assumed
average speed. In fact, du Plessis (1989) proved that there are huge differences in
repair and maintenance cots depending on whether urban or rural speed is assumed.
Because of the above points, the research team selected the HDM 4 model for further
evaluation and discussion.
1- Ease of use and availability of appropriate input data: The literature review presented earlier
suggests that current repair/maintenance models are empirical in nature because of wide
variations in maintenance practices and long term data requirements among other factors.
C-8
This is because of the following (Bennett and Greenwood 2000):
– The costs usually arise infrequently over the life of the vehicle;
– The maintenance practices of the owners/operators have a major impact on the costs;
– The maintenance costs for similar vehicles can vary significantly among
manufacturers;
– Vehicles operating in harsh conditions may be of more robust construction and
therefore have lower maintenance costs than standard vehicles, and;
– Correlating maintenance costs with operating conditions is difficult since vehicles
tend to operate over a range of roads; therefore the costs are averaged out.
It should also be noted that the estimation of parameters for the parts model is
particularly problematic. There is a general consensus opinion that improvements in vehicle
technology and other factors given modern technology lower maintenance costs, and that
modern technology vehicles are also less sensitive to roughness. However, there have been
relatively few studies done to verify this consensus opinion, particularly with passenger cars.
Given its empirical nature, the input variables for the model are available from different
sources of data.
2- The ability of the model to incorporate pavement surface conditions as currently being
measured: The input variables related to pavement surface conditions (e.g. roughness) are as
currently being measured. In fact, the model characterizes roughness by the International
Roughness Index.
3- Reasonableness and applicability to U.S. conditions: Repair and maintenance data from
vehicle fleets as reported in NCHRP 1-33 were correlated with pavement condition (IRI) and
compared with HDM 4 predictions (Figure C-1). It can be seen that:
• The parts consumption according to NCHRP1-33 is lower than the predictions by
HDM 4.
• The labor hours according to NCHRP 1-33 are much lower than those predicted by
HDM 4.
The predictions from HDM 4 do not appear to be reasonable for US conditions. These
observations could be explained by the fact that HDM 4 model was calibrated using data from
C-9
developing countries (e.g., Brazil, India). It is well known that the labor hours in those countries
are much higher than in the US. Also, the difference between parts consumption in the US and
those predicted from HDM 4 could be explained by the inflation in the parts and vehicle prices.
For the most part, it is expected that the bulk of any correction/calibration to match US
conditions could be achieved using macro-economic model corrections based on overall
(average) economic data (e.g., average labor hours for typical vehicles and average parts cost
comparisons). Therefore, in this research, the latest comprehensive research conducted in the US,
i.e. Zaniewski’s tables/charts, was updated to current conditions.
(a) Parts Consumption
(b) Labor Hours
1 m/km = 63.4 in/mile
Figure C-1 Comparison between HDM-4 Predictions and Data from Truck Fleets (NCHRP 1-33)
0
0.01
0.02
0.03
0.04
0.05
0 1 2 3 4 5 6
Part
s C
ost (
$/km
)
IRI (m/km)
0
0.25
0.5
0.75
1
0 1 2 3 4 5 6
Labo
r Cos
t ($/
km)
IRI (m/km)
( )NCHRP 1-33 HDM 4
C-10
C2 - TYPICAL US CONDITIONS: INPUT DATA SOURCES AND
DISTRIBUTIONS FOR REPAIR AND MAINTENANCE
Tables C-4 and C-5 present the different input parameters for repair and maintenance
costs and their sources. Pavement condition and traffic data are readily available. Vehicle
characteristics data and the output data are not readily available. These data have been collected
from different sources which include NCHRP 1-33 (Truck fleets), and Texas and Michigan
DOTs.
Table C-4 Input parameters for the repair and maintenance model Pavement Condition Traffic Vehicle Characteristics
Parts consumption • IRI • Congestion • Vehicle Type • Cumulative mileage • Vehicle acceleration
Labor hours Same as Parts Consumption
Table C-5 Data sources for input parameters of the repair and maintenance model Input parameters Data Sources Method Status
Pavement Condition
• IRI • HPMS • NCAT
• Records Collected
Traffic • Congestion • SHA • Records Available
Vehicle Characteristics
• Vehicle Type • Cumulative mileage • Vehicle acceleration
• SHA + other fleets • NCAT
• Records Collected
MICHIGAN DEPARTMENT OF TRANSPORTATION (MDOT) DATA
Pavement condition, vehicle characteristics and repair and maintenance costs data have
been collected from Michigan DOTs. Figure C-2 shows a map view of the eight district of
Michigan.
C-11
Figure C-2 Michigan Regions
Figure C-3 shows the distribution of roughness in terms of Ride Quality Index (RQI) in
the eight regions of Michigan. RQI reflects the user’s perception about pavement ride quality.
According to Lee et al. (2002), RQI and IRI have a good correlation, with the RQI increasing
asymptotically with increasing IRI to a plateau value of about 100 as the IRI-values approach the
4 to 5 m/km range. The IRI-value corresponding to an RQI of 70 is about 2.4 m/km (threshold
for rehabilitation in Michigan). Figure C-4 shows the percent of sections with IRI > 2.4 m/km.
Figure C-5 shows the distribution of repairs by region for different vehicle classes. Figure
C.6 shows the average labor and parts costs versus roughness for different vehicle classes for
Michigan. It should be noted that the graphs were corrected for mileage. A data point in the
graphs represents the average labor hours or parts costs for all the vehicles within a district for
each vehicle class.
C-12
(a) Superior region
(b) North region
(c) Grand region
(d) Bay region
(e) Southwest region
(f) University region
(g) Metro region
Figure C-3 Distribution of Roughness for Michigan Regions (Michigan DOT, 2001)
020406080
100
10 40 70 100 More
RQI
Freq
uenc
y
0
50
100
150
200
10 40 70 100 More
RQI
Freq
uenc
y
0102030405060
10 40 70 100 More
RQI
Freq
uenc
y
0
10
20
30
40
10 40 70 100 More
RQI
Freq
uenc
y
0
20
40
60
80
10 40 70 100 More
RQI
Freq
uenc
y
01020304050
10 40 70 100 More
RQI
Freq
uenc
y
0
5
10
15
10 40 70 100 More
RQI
Freq
uenc
y
C-13
Figure C-4 Percent of Sections with IRI > 2.4 m/km
Figure C-5 Distribution of Repairs by District and Vehicle Class (Michigan DOT)
0%
10%
20%
30%
40%
50%
60%
superior north grand bay southwest university metro
Region
% o
f sec
tions
with
IRI >
2.4
m/k
m
0
500
1000
1500
2000
2500
3000
3500
4000
superior north grand bay southwest university metro
Regions
Num
ber o
f veh
icle
Passenger Cars Light Trucks Medium Trucks
C-14
(a) Passenger cars
(b) Light Trucks
(c) Medium Trucks
Figure C-6 Summary of the Repair and Maintenance Data (Michigan DOT)
$0$100$200$300$400$500$600
0.00 0.50 1.00 1.50 2.00 2.50
IRI (m/Km)
Parts
Con
sum
ptio
n ($
)
$0
$100
$200
$300
$400
Labo
r Cos
t ($)
$0
$500
$1,000
$1,500
$2,000
$2,500
0.00 0.50 1.00 1.50 2.00 2.50 3.00
IRI (m/Km)
Parts
Con
sum
ptio
n ($
)
$0
$500
$1,000
$1,500
$2,000
$2,500
Labo
r Cos
t ($)
$0$1,000$2,000$3,000$4,000$5,000
0.00 1.00 2.00 3.00
IRI (m/Km)
Parts
Con
sum
ptio
n ($
)
$0$1,000$2,000$3,000$4,000$5,000
Labo
r Cos
t ($)
C-15
TEXAS DEPARTMENT OF TRANSPORTATION (MDOT) DATA
The state of Texas has 25 congressional districts. Figure C-7 shows a map view of Texas
districts. Figure C.8 shows the average roughness measured during the financial year 2006 for
each district of the state of Texas. It should be noted that there is 100% roadbed coverage that
includes all state maintained road network, i.e. interstate, national and state road and farm-to-
market network. Figures C.9 through C.11 show the distribution of repairs by district (Texas) for
different vehicle classes. Figures C.12 and C.13 show the average labor and parts costs by
district for different vehicle classes. It should be noted that the graphs present the corrected data
for age and mileage. A data point in the graphs represents the median values of labor or parts
costs for all the vehicles within a district for each vehicle class.
Figure C-7 Texas Districts
District PHARR PHR 1 ODESSA ODA 2 ATLANTA ATL 3 AUSTIN AUS 4 HOUSTON HOU 5 LUBBOCK LBB 6 SAN ANGELO SJT 7 AMARILLO AMA 8 WACO WAC 9 CHILDRESS CHS 10 BEAUMONT BMT 11 FORT WORTH FTW 12 YOAKUM YKM 13 WICHITA FALLS WFS 14 BROWNWOOD BWD 15 SAN ANTONIO SAT 16 TYLER TYL 17 ABILENE ABL 18 BRYAN BRY 19 CORPUS CHRISTI CRP 20 EL PASO ELP 21 PARIS PAR 22 LAREDO LRD 23 LUFKIN LFK 24 DALLAS DAL 25
code
C-16
Figure C-8 Distribution of IRI by District for Texas (Texas DOT)
Figure C-9 Distribution of Repairs by District for Passenger Cars (Texas DOT)
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25District code
IRI (
m/k
m)
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
District code
Num
ber o
f Pas
seng
er c
ars
C-17
(a) Light truck
(b) Medium trucks
Figure C-10 Distribution of Repairs by District (Texas DOT)
0
100
200
300
400
500
600
700
800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
District code
num
ber
of L
ight
Tru
cks
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
District code
num
ber o
f Med
ium
Tru
cks
C-18
(a) Heavy truck
(b) Articulated truck
Figure C-11 Distribution of Repairs by District (Texas DOT)
0
20
40
60
80
100
120
140
160
180
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
District code
num
ber o
f Hea
vy T
ruck
s
0
2
4
6
8
10
12
14
16
18
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
District code
num
ber o
f Arti
cula
ted
Truc
ks
C-19
(a) Passenger Cars
(b) Light Trucks
(c) Medium Trucks
Figure C-12 Summary of the Repair and Maintenance Data for Light and Medium Vehicles (Texas DOT)
$0
$200
$400
$600
$800
1.40 1.60 1.80 2.00 2.20 2.40
IRI (m/km)
Parts
cos
t ($)
$0
$200
$400
$600
$800
Labo
r cos
ts ($
)$0
$200
$400
$600
$800
$1,000
1.4 1.6 1.8 2 2.2 2.4
IRI (m/km)
Parts
cos
t ($)
$0
$200
$400
$600
$800
$1,000
Labo
r cos
ts ($
)
$0$200$400$600$800
$1,000$1,200
1.4 1.6 1.8 2 2.2 2.4
IRI (m/km)
Parts
($)
$0$200$400$600$800$1,000$1,200
Labo
r cos
ts ($
)
C-20
(a) Heavy Trucks
(b) Articulated Trucks
(c) Buses
Figure C-13 Summary of the Repair and Maintenance Data for Heavy Vehicles (Texas DOT)
$0
$200
$400
$600
$800
$1,000
1.4 1.6 1.8 2 2.2 2.4
IRI (m/km)
Parts
cos
ts ($
)
$0
$200
$400
$600
$800
$1,000
Labo
r cos
ts ($
)$0
$500
$1,000
$1,500
$2,000
1.4 1.6 1.8 2 2.2 2.4
IRI (m/km)
Parts
cos
t ($)
$0
$500
$1,000
$1,500
$2,000
Labo
r cos
ts ($
)
$0
$200
$400
$600
$800
1.4 1.6 1.8 2 2.2 2.4
IRI (m/km)
Parts
Cos
ts ($
)
$0
$200
$400
$600
$800
Labo
r cos
ts ($
)
C-21
NCHRP 1-33 DATA
Data from previous research conducted as part of NCHRP 1-33 project were also
obtained. It should be noted that the data is only for heavy trucks. Figures C.14 shows the raw
data and Figure C.15 show the corrected data for mileage and age.
(a) Annual Parts Cost versus Age
(b) Annual Labor Cost versus Age
Figure C-14 NCHRP 1-33 Raw Data (Papagiannakis, 2000)
0
2000
4000
6000
8000
10000
12000
14000
0 1 2 3 4 5 6 7
AGE yrs
ANNU
AL C
OST
OF
R/M
PAR
TS $
0
20
40
60
80
100
120
140
160
180
0 1 2 3 4 5 6 7AGE (yrs)
AN
UA
L C
OS
T O
F R
/M L
AB
OR
hrs
C-22
(c) Cost of Parts per 1000 km
(d) Labor Hours per 1000 km
Figure C-15 NCHRP 1-33 Data Corrected for Age and Mileage (Pappagianakis, 2000)
0
5
10
15
20
25
30
35
40
45
50
1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
ROUGHNESS IRI m/km
COST
OF
PART
S $/
1000
km
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
ROUGHNESS IRI m/km
LABO
R hr
s/10
00 k
m
C-23
C3 - UPDATED ZANIEWSKI ET AL TABLES
Table C-6 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Small Car
Table C-7 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Medium Car
Table C-8 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Large Car
Table C-9 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Pickup and Van
Table C-10 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Light Truck
Table C-11 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Medium Truck, Heavy Truck and Bus
Table C-12 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Articulated Truck
C-24
Table C-6 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Small Car
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 30.4 32.3 34.6 37.2 40.2 43.4 46.6 50.0 53.4 57.0 60.7 64.4 68.8 73.8 7 30.3 32.0 34.1 36.6 39.4 42.4 45.5 48.8 52.1 55.4 58.9 62.5 66.9 71.9 6 30.1 31.7 33.6 35.9 38.6 41.5 44.4 47.5 50.6 53.9 57.3 60.9 65.0 69.4 5 30.0 31.4 33.1 35.2 37.9 40.6 43.3 46.2 49.3 52.3 55.5 58.9 62.5 67.5 4 29.8 31.1 32.7 34.7 37.1 39.6 42.3 44.9 47.8 50.8 53.8 57.1 60.8 65.0 3 29.7 30.8 32.2 34.1 36.3 38.7 41.1 43.7 46.4 49.2 52.1 55.2 58.8 63.1 2 29.5 30.4 31.8 33.4 35.5 37.8 40.1 42.4 45.0 47.6 50.4 53.3 56.7 60.7 1 29.4 30.1 31.3 32.8 34.8 36.8 38.9 41.2 43.6 46.1 48.6 51.4 54.7 58.5 0 29.2 29.8 30.8 32.2 33.9 35.8 37.9 39.9 42.2 44.5 46.9 49.6 52.6 56.3 -1 29.1 29.5 30.3 31.6 33.2 34.9 36.8 38.7 40.8 42.9 45.2 47.7 50.6 54.1 -2 25.0 24.1 22.7 20.7 18.1 33.9 35.6 37.4 39.4 41.4 43.5 45.8 48.6 51.9 -3 39.8 38.9 37.4 35.4 32.9 29.6 25.6 20.8 37.9 39.8 41.8 43.9 46.5 49.8 -4 54.4 53.6 52.2 50.2 47.6 44.3 40.3 35.6 30.1 24.0 40.1 42.1 44.5 47.6 -5 69.4 68.1 66.9 65.0 62.4 59.1 55.1 50.3 44.9 38.8 31.9 24.4 42.4 45.4 -6 83.8 83.1 81.9 79.4 76.9 73.8 70.0 65.0 59.6 53.5 46.6 39.1 31.0 43.2 -7 98.8 98.1 83.8 94.4 91.9 88.8 84.4 80.0 74.4 68.1 61.4 53.8 45.8 37.2 -8 113.8 112.5 111.3 109.4 106.9 103.1 99.4 94.4 89.4 83.1 76.3 68.8 60.4 51.9
C-25
Table C-7 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Medium Car
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 30.8 32.6 35.1 37.9 41.0 44.3 47.7 51.3 54.9 58.6 58.0 66.3 70.6 76.3 7 30.6 32.3 34.5 37.2 40.4 43.3 46.5 49.8 53.3 56.8 60.5 64.4 68.8 73.8 6 30.4 31.9 34.0 36.4 39.3 42.2 45.3 48.4 51.7 55.1 58.6 62.3 66.3 71.3 5 30.2 31.6 33.5 35.8 38.4 40.6 44.1 47.0 50.1 53.3 56.6 60.2 64.4 68.8 4 30.1 31.3 32.9 35.1 37.5 40.1 42.8 45.6 48.6 51.6 54.7 58.1 61.8 66.3 3 29.9 30.9 32.4 34.4 36.6 39.1 41.6 44.3 47.0 49.8 52.8 55.9 59.6 63.8 2 29.7 30.6 31.9 33.7 35.8 38.0 40.4 42.8 45.4 48.1 50.9 53.9 57.3 61.3 1 29.5 30.2 31.4 32.9 34.9 36.9 39.1 41.4 43.8 46.3 48.9 51.8 55.0 58.9 0 29.3 29.9 30.9 32.3 34.0 35.9 37.9 40.0 42.3 44.6 47.0 49.6 52.7 56.4 -1 29.2 29.5 30.3 31.6 33.1 34.8 36.7 38.6 40.7 42.8 45.1 47.6 50.4 53.9 -2 24.4 23.6 22.3 20.4 18.1 33.8 35.4 37.3 39.1 41.1 43.1 45.4 48.1 51.5 -3 39.1 38.3 36.9 35.1 32.8 29.7 26.0 21.7 37.6 39.3 41.2 43.4 45.9 49.1 -4 53.8 52.9 51.6 49.8 47.4 44.4 40.7 36.4 31.3 25.6 39.3 41.3 43.6 46.6 -5 68.1 67.5 66.3 64.4 62.1 59.1 55.4 51.0 46.0 40.3 33.9 27.1 41.3 44.1 -6 83.1 82.5 81.3 79.4 76.9 73.8 70.0 65.6 60.7 55.0 48.6 41.7 34.2 41.7 -7 97.5 96.9 95.6 93.8 91.3 88.1 85.0 80.6 75.6 69.4 63.1 56.4 49.2 41.0 -8 112.5 111.9 110.6 108.8 106.3 103.1 99.4 95.0 90.0 84.4 78.1 71.3 63.8 55.7
C-26
Table C-8 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Large Car
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 31.1 33.1 35.5 38.4 41.6 44.9 48.4 52.0 55.7 59.4 63.1 67.5 71.9 76.9 7 30.9 32.8 34.9 37.6 40.7 43.9 47.1 50.6 54.1 57.6 61.4 65.0 69.4 74.4 6 30.7 32.4 34.4 36.9 39.8 42.8 45.9 49.1 52.4 55.8 59.4 63.1 67.5 70.6 5 30.5 32.0 33.9 36.2 38.9 41.7 44.6 47.7 50.8 54.1 57.4 61.0 65.0 69.4 4 30.3 31.6 33.4 35.5 38.0 40.6 43.4 46.3 49.3 52.3 55.4 58.9 62.5 66.9 3 30.1 31.3 32.8 34.8 37.1 39.6 42.1 44.8 47.6 50.4 53.5 56.7 60.3 64.4 2 30.0 30.9 32.3 34.1 36.2 38.5 40.9 43.4 46.0 48.6 51.5 54.6 58.0 62.1 1 29.8 30.6 31.8 33.4 35.3 37.4 39.6 41.9 44.4 46.9 49.5 52.4 55.7 59.6 0 29.6 30.2 31.2 32.6 34.4 36.3 38.4 40.5 42.8 45.1 47.6 50.3 53.3 57.1 -1 29.4 29.9 30.7 31.9 33.5 35.3 37.1 39.1 41.1 43.3 45.6 48.1 51.0 54.6 -2 211.6 23.4 22.3 20.8 18.8 34.2 35.8 37.6 39.6 41.5 43.6 45.9 48.7 52.1 -3 37.6 37.0 35.9 34.3 32.3 29.8 26.6 22.8 37.9 39.7 41.6 43.8 46.3 49.6 -4 51.2 50.6 49.4 47.9 45.9 43.3 40.1 36.4 32.1 27.1 39.7 41.6 44.0 47.0 -5 65.0 64.4 63.1 61.5 59.4 56.9 53.7 49.9 45.6 40.7 35.2 29.1 41.7 44.5 -6 78.1 77.5 76.9 75.0 73.1 70.6 67.5 63.8 59.2 54.3 48.8 42.8 36.3 29.4 -7 91.9 91.3 90.0 88.8 86.9 83.8 80.6 76.9 72.5 68.1 62.3 56.3 49.8 43.0 -8 105.6 105.0 103.8 102.5 100.0 97.5 94.4 90.6 86.3 81.3 75.6 70.0 63.1 56.6
C-27
Table C-9 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Pickup and Van
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 30.7 33.0 35.4 38.1 41.2 44.4 47.8 51.3 54.9 58.6 62.4 66.3 70.6 75.6 7 30.9 32.7 34.8 37.4 40.2 43.4 46.6 50.0 53.4 56.9 60.6 64.4 68.8 73.8 6 30.8 32.4 34.4 36.8 39.5 42.4 45.0 48.6 51.9 55.3 58.7 62.4 66.3 71.3 5 30.6 32.0 33.9 36.1 38.7 41.4 44.3 47.3 50.4 53.6 56.9 60.4 64.4 68.8 4 30.4 31.7 33.4 35.4 37.9 40.4 43.2 46.0 48.9 51.9 55.1 58.4 62.2 66.9 3 30.3 31.4 33.0 34.8 37.0 39.5 42.0 44.7 47.4 50.3 53.3 56.4 60.1 64.4 2 30.1 31.0 32.4 34.1 36.2 38.5 40.9 43.4 45.9 48.6 51.4 54.5 57.9 62.0 1 29.9 30.7 31.9 33.4 35.4 37.5 39.7 42.0 44.4 46.9 49.6 52.5 55.8 59.7 0 29.8 30.4 31.4 32.8 34.6 36.5 38.6 40.7 43.0 45.3 47.8 50.5 53.6 57.3 -1 27.1 30.1 30.9 32.1 33.7 35.4 37.4 39.4 41.5 43.6 45.9 48.5 51.4 55.0 -2 19.6 18.8 17.4 15.6 33.1 34.5 36.1 38.1 40.0 41.9 44.1 46.5 49.3 52.7 -3 34.6 33.8 32.4 30.5 28.1 24.9 21.2 36.8 38.5 40.3 42.3 44.6 47.1 50.4 -4 49.6 48.8 46.1 45.5 43.0 39.9 36.1 31.7 26.6 38.6 40.5 42.6 44.9 48.1 -5 64.4 63.8 62.4 60.4 58.0 54.9 51.1 46.6 41.5 35.7 30.4 40.6 42.8 45.8 -6 79.4 78.8 77.5 75.6 73.1 70.0 66.3 61.6 56.5 50.6 44.1 36.9 29.3 43.4 -7 94.4 93.8 92.5 90.6 88.1 85.0 81.3 76.9 71.3 65.6 59.1 52.0 44.3 36.3 -8 109.4 108.8 107.5 105.6 103.1 100.0 96.3 91.9 86.3 80.6 74.4 66.9 59.3 51.2
C-28
Table C-10 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Light Truck
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 32.2 35.3 39.4 44.3 49.8 55.8 62.2 68.8 75.6 83.1 90.6 97.5 105.0 112.5 7 31.8 34.3 38.0 42.4 47.5 53.1 59.0 65.0 71.9 78.8 85.6 91.9 98.8 105.6 6 31.3 33.4 36.6 40.6 45.3 50.3 55.8 61.6 67.5 73.8 80.6 86.9 93.1 99.4 5 30.8 32.5 35.3 38.8 42.9 47.6 52.6 57.9 63.8 69.4 75.6 81.3 87.5 93.1 4 30.4 31.6 33.9 37.0 40.7 44.9 49.4 54.3 59.5 66.3 70.6 75.6 81.3 86.9 3 29.9 30.7 32.6 35.2 38.3 42.1 46.3 50.7 55.4 60.3 65.6 70.6 75.6 80.6 2 29.4 29.8 31.2 33.4 36.1 39.4 43.1 47.1 51.3 55.8 60.3 65.0 69.4 74.4 1 29.0 28.9 29.8 31.6 33.8 36.7 39.9 43.4 47.3 51.2 55.3 59.4 63.8 67.5 0 28.6 27.9 28.4 29.8 31.6 33.9 36.7 39.8 43.2 46.7 50.3 54.0 57.7 61.4 -1 10.4 9.3 27.1 27.9 29.3 31.2 33.6 36.2 39.1 42.1 45.3 48.5 51.8 55.0 -2 20.3 19.3 18.3 17.3 14.9 15.0 13.6 12.1 35.0 37.6 40.3 43.1 45.9 48.1 -3 30.2 29.2 28.3 27.3 26.1 24.9 23.6 22.0 20.3 18.4 16.2 37.6 39.9 41.9 -4 40.1 39.1 38.1 37.1 36.1 34.8 33.5 31.9 30.3 28.3 26.1 23.8 21.0 35.6 -5 50.1 49.0 48.1 47.1 46.0 44.8 43.4 41.9 40.2 38.3 36.1 33.1 30.9 28.0 -6 60.0 58.9 58.0 57.0 55.9 54.7 53.3 51.8 50.1 48.1 46.0 43.6 40.9 37.9 -7 70.0 68.8 68.1 66.9 65.6 64.4 63.1 61.8 60.0 58.1 55.9 53.5 50.8 47.9 -8 80.0 78.8 78.1 76.9 75.6 74.4 73.1 71.9 70.0 68.1 65.6 63.1 60.8 57.8
C-29
Table C-11 Updated Repair and Maintenance Costs (% avg cost/1000 km) – MediumTruck, Heavy Truck and Bus
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 32.4 36.8 41.1 45.7 50.4 55.4 60.5 65.6 71.9 77.5 83.8 90.6 97.5 105.0 7 32.0 35.8 39.8 43.9 48.1 52.6 57.3 62.3 67.5 73.1 78.8 85.0 91.9 98.8 6 31.5 34.9 38.4 42.1 45.9 49.9 54.1 58.6 63.1 68.1 73.8 79.4 85.6 92.5 5 31.1 34.0 37.0 40.2 43.6 47.1 50.9 54.9 59.2 63.8 68.8 73.8 80.0 85.6 4 30.6 33.1 35.6 38.4 41.3 45.0 47.7 51.3 55.1 59.2 63.8 68.8 73.8 79.4 3 30.1 32.2 34.3 36.6 39.0 41.6 44.4 47.6 50.9 54.6 58.7 63.1 68.1 73.1 2 29.7 31.3 32.9 34.7 36.7 39.5 41.3 43.9 46.8 50.1 53.6 57.6 61.9 66.9 1 29.3 30.3 31.5 32.9 34.4 36.1 38.1 40.3 42.7 45.4 48.6 53.3 55.6 60.2 0 28.8 29.4 30.1 31.1 32.1 33.4 34.8 36.6 38.6 40.9 43.6 46.6 49.9 53.8 -1 10.4 9.9 9.4 29.2 29.8 30.6 31.6 32.9 34.4 36.3 38.5 41.1 44.0 47.4 -2 19.6 19.1 18.6 18.1 17.4 16.6 15.9 15.0 14.1 13.2 33.4 35.6 38.1 40.9 -3 28.8 28.3 27.8 27.2 26.6 25.8 25.0 24.2 23.3 22.4 21.4 20.4 19.5 18.5 -4 37.9 37.5 37.0 36.4 35.8 35.0 34.2 33.4 32.4 31.6 30.6 29.6 28.7 27.7 -5 47.1 46.6 46.1 45.6 44.9 44.1 43.4 42.5 41.6 40.7 39.8 38.8 37.8 36.9 -6 56.3 55.8 55.3 54.8 54.1 53.3 52.6 51.7 50.8 49.9 48.9 48.0 47.0 46.0 -7 65.6 65.0 64.4 63.8 63.1 62.5 61.7 60.9 60.0 59.1 58.1 57.1 56.2 55.2 -8 74.4 74.4 73.8 73.1 72.5 71.9 70.6 70.0 69.4 68.1 67.5 66.3 65.6 64.4
C-30
Table C-12 Updated Repair and Maintenance Costs (% avg cost/1000 km) – Articulated Truck
Grade %
Speed (km/h)
8 16 24 32 40 48 56 64 72 80 88 96 104 112
8 35.1 41.1 48.0 55.6 63.8 72.5 81.3 90.6 100.6 110.6 120.0 130.6 140.6 150.6 7 34.3 39.5 45.6 52.4 59.8 67.5 76.3 84.4 93.1 102.5 111.9 120.6 130.0 139.4 6 33.4 37.9 43.3 49.3 55.8 63.1 70.6 78.1 86.3 94.4 103.1 111.3 120.0 128.8 5 32.7 36.3 40.9 46.1 51.9 58.1 65.0 71.9 79.4 86.9 94.4 101.9 109.4 117.5 4 31.9 34.8 38.5 42.9 47.9 53.4 59.4 65.6 71.9 78.8 85.6 92.5 99.4 106.3 3 31.1 33.2 36.1 39.8 43.9 48.6 53.8 59.3 65.0 70.6 76.9 83.1 88.8 95.0 2 30.3 31.6 33.8 36.6 40.0 43.9 48.3 52.9 57.8 63.1 68.1 73.1 78.8 84.4 1 29.5 30.0 31.4 33.4 36.1 39.2 42.8 46.6 50.8 55.1 59.4 63.8 68.8 73.1 0 28.7 28.4 29.0 30.3 32.1 34.4 37.3 40.3 43.6 47.1 50.8 54.5 58.2 62.1 -1 10.8 10.4 10.1 9.9 9.8 29.7 31.7 34.0 36.5 39.3 42.1 45.0 47.9 51.0 -2 20.7 20.3 20.0 19.8 19.6 19.4 19.3 19.0 18.7 18.3 17.8 17.1 37.6 39.9 -3 30.6 28.9 29.9 29.7 29.5 29.3 29.1 28.9 28.6 28.2 27.6 27.0 26.3 25.4 -4 40.5 40.1 39.8 39.6 39.4 39.2 39.0 38.8 38.4 38.1 37.6 36.9 36.1 35.3 -5 50.4 49.9 49.7 49.4 49.3 49.1 48.9 48.6 48.3 47.9 47.4 46.8 46.1 45.2 -6 60.3 59.9 59.6 59.4 59.2 58.9 58.8 58.6 58.3 57.8 57.3 56.7 55.9 55.1 -7 70.0 70.0 69.4 69.4 69.4 68.8 68.8 68.1 68.1 67.5 67.5 66.9 65.6 65.0 -8 80.0 79.4 79.4 79.4 78.8 78.8 78.8 78.1 78.1 77.5 76.9 76.3 75.6 75.0
C-31
C4 - MODELS FOR GENERATION OF ARTIFICIAL ROAD
PROFILES
ARTIFICIAL ROAD PROFILE GENERATION
Various types of road models have been in use for years to represent roads for analyzing
vehicle ride behavior (Gillespie and Sayers, 1981). One of the first proposed stochastic models is
an equation of the form:
( )( )22
AGz vvπ
= ( C.15)
Where, Gz(v) = PSD function of elevation (z) v =Wavenumber A = Roughness coefficient obtained by fitting the OSD of a measured road to the above
equation
As the elevation is perceived to be changing with time, it also has a velocity (proportional
to slope) and acceleration (proportional to the derivative of slope), which also have PSDs for the
same road section. Since velocity is the derivative of position, the velocity PSD is related to the
elevation PSD by the scale factor (2πv). And, likewise, the acceleration PSD is related to the
velocity PSD by the same scale factor. The concept of the road as an acceleration input to the
vehicle is important to understand because its ultimate effect - vehicle ride vibration- is
invariably quantified as accelerations (Gillespie and Sayers, 1981). Gillespie et al. (1993)
proposed the following equation for the PSD model:
( )( ) ( )4 22 2
Ga GsGz v Gev vπ π
= + + ( C.16)
The first component, with the amplitude Ga, is a white noise acceleration that is
integrated twice. The second, with amplitude Gs, is a white noise slope that is integrated once.
The third, with amplitude Ge, is a white noise elevation. The model can also be written to define
the PSD of profile slope by looking at the derivative of the above equation.
C-32
( )( )
( )2'2 2
2GaGz v Gs Ge v
vπ
π= + +
( C.17)
Gillespie et al. (1993) suggested ranges of roughness parameters based on the road
profiles measured in North America, England and Brazil (Table C-13). When traversed by a
vehicle, the profile is perceived as an elevation that changes with time, where time and
longitudinal distance are related by the speed of the vehicle. The time-varying elevation can also
be characterized by a PSD that has units of elevation.
Table C-13 Roughness Parameters for the White-Noise PSD Model (Gillespie et al., 1993)
SURFACE TYPE Gs
(m/cycle× 10-6)
Ga
(1/m cycle ×10-6
)
Ge
(m3/cycle ×10-6)
Asphalt (Ann Arbor) 1~300 0.0~7 0.0~8.0
Asphalt (Brazil) 4~100 0.4~4 0.0~0.5
PCC (Ann Arbor) 4~ 90 0.0~1 0.0~0.4
Surface treatment (Brazil) 8~ 50 0.0~4 0.2~1.2
Marcondes et al. (1991) developed another equation to predict PSD. Elevation profiles of
federal and interstate highways near Lansing, Michigan, were measured with a profilometer and
PSD were calculated. They developed the following equations to fit the above data:
( ) ( )
( ) ( )1 1
2 0 1
,
,
kvpPDpe v A e v v
PDpe v A v v v v
−= ≤
= − > ( C.18)
Where,
PDpe(v) = Power density value [in3/cycle] for the pavement elevation v1 = Discontinuity frequency, cycles/in v0 = Asymptote frequency, cycles/in A1, A2 = Constants k, p, q = Constants
Marcondes (1990) reported the range of values of parameters for the equations as shown
in Table C-14.
C-33
Table C-14 Ranges of Variable Values for PSD Equations (Marcondes, 1990)
Category A1 k p A2 v0 q
OC* 1.3~7.2 7000~ 67000
1.6~2.0 5.9E-7~ 4.2E-5
0~ 3.9E-3
-2.6~ -1.5
NC* 1.5~3.4 24000~ 83000
1.8~2.0 6.0E-7~ 6.0E-5
2.5E-3~ 4.9E-3
-2.2~ -1.1
AC* 1.8~5.7 63000~ 240000
2.0~2.2 1.4E-4~ 7.7E-4
4.6E-3~ 5.2E-3
-1.1~ -0.5
*Note: OC: old concrete pavement; NC: new concrete pavement; and AC: asphalt concrete pavement.
They investigated the relationship between RMS (Root Mean Square) elevation and IRI
(International Roughness Index); the measured data showed that the correlation between them is
weak (R2 <0.7). It was found that a good correlation exists between the IRI and the PSD only for
spatial frequencies between 0.002 and 0.015 cycle/in.
From the same data, Marcondes et al. (1992) found strong correlations between the IRI
and the RMS vertical acceleration at the truck bed. The following models were developed for
these relationships:
- For vehicle speed = 45 mph: 2 3 7 2 23.794 10 1.902 10 8.89 10 0.937RMS IRI IRI R− − −= × + × × − × × =
( C.19)
- For vehicle speed = 52mph: 2 3 6 2 24.467 10 2.144 10 1.819 10 0.914RMS IRI IRI R− − −= × + × × − × × =
( C.20)
- For vehicle speed = 60 mph: 3 6 2 20.105 1.25 10 1.63 10 0.866RMS IRI IRI R− −= + × × − × × =
( C.21)
Most researchers have used generated road profiles for dynamic vehicle simulation. Road
profiles, like any other random signal, can be generated using a random number algorithm. To
generate random numbers, Gillespie et al. (1993) used a Gaussian distribution with a mean value
of zero, and the standard deviation is:
1/2
2Gs = ∆
( C.22)
C-34
Where, G = White-noise amplitude for one of the three coefficients; Gs, Ge and Ga Δ = Interval between samples used for wavenumber.
A simulated road profile that matches the target PSD is generated using the following
procedures:
1) Create an independent sequence of random numbers for each of the three white-noise sources, scaled according to the above equation.
2) Integrate each sequence as needed to obtain the desired distribution over wavenumber.
3) Sum the outputs of the filters.
Thus, the sequence corresponding to the Ga term is integrated twice, the sequence
corresponding to the Gs term is integrated once, while the sequence corresponding to the Ge
term is not integrated. Table C-15 shows PSD coefficients and IRI values used by Gillespie et al.
(1993).
Table C-15 PSD Coefficients in the Roughness Model (Gillespie et al, 1993)
Pavement Type
Surface Type
IRI (in/mi)
Gs (m/cycle×10-6)
Ga 1/(m×cycle×10-6)
Ge (m3/cycle×10-6)
Smooth 75 6 0.00 0.000
Flexible Medium 150 12 0.17 0.000
Rough 225 20 0.20 0.003
Smooth 80 1 0.00 0.000
Rigid Medium 161 20 0.25 0.100
Rough 241 35 0.30 0.100
In the case of a rigid pavement, faulting and curling/warping should be considered. The
slab roughness between joints has similar characteristics to that of a flexible pavement; and,
therefore, the periodic faults caused by the slab joints are superimposed on to this model. The
resulting road profile over a slab length is:
C-35
( ) ( ) ( )jx y x y xr fy = + (C.23)
Where,
yr(x) = The profile due to the slab roughness
yjf (x) = 0
0
h x for x LL
for x L
< < =
h = Joint fault magnitude
L = Joint spacing
Thermal and moisture gradient across the slab thickness result in significant bending
moments along the edges of the slab. The curling/warping is modeled as a periodic hemispheric
wave added to the above road model:
( ) ( )22 2
2 2;2 2 2L L Lx y x R R Rw δ δ − + − − = = + −
(C.24)
Where,
δ = Mid-slab deflection due to warping R = Radius of curvature of slab
yw = Vertical displacement due to warping L = Joint spacing The final road profile is:
( ) ( ) ( ) ( )r wjfy x y x y x y x= + + (C.25)
ARTIFICIAL GENERATION OF ROUGHNESS FEATURES
The identified roughness features that were proven to affect vehicle suspensions are:
Faulting, breaks and curling in concrete pavements and potholes in asphalt pavements. To
investigate their effect, these roughness features were artificially generated and superimposed on
to the generated road surface profile. Figure C-16 presents schematic description of roughness
features.
C-36
(a) Curb
(b) Fault
(c) Break
(d) Bump
(e) Pothole
(f) Curling
Figure C-16 Schematic Description of Roughness Features
Artificial Generation of Faults Faulting is the difference in elevation across a joint or crack. It is determined by
measuring the difference in elevation between the approach slab and the adjacent slab. In the
current practice of road surface profile measurement in the US, the reporting interval for
elevation is 0.025 to 0.075 m (1 to 3 inches). Based on previous study by Chatti et al. (2009), for
a sampling interval of 0.019 m and a reporting interval of 0.075 m, the correct height of a fault is
detectable when it is calculated as the difference in elevation between points that are 0.15 m
apart. Accordingly, the width of a fault was taken as this value. The general form of a fault is
given by Equation C.25. Figure C-17 shows the form used to find the mathematical description
of a fault.
C-37
( )
( )
( )
( )
16001
For :
0 0.150.15
0.150.15
For :
0 0.150.15
0.150.15
0
f
f
fl
N
l Lhu x x for x
h x l for x ll
l Lhu x x for x
h x L for x LL
for x L
=−
≤
= − ≤ ≤ − ≤ ≤ −
≥
= − ≤ ≤ − ≤ ≤ −
≥
(C.26)
Where,
uf (x) = Road profile due to fault
Nf = Number of faults per 1.6 km h = Joint fault magnitude in mm l = Distance between faults L = Joint spacing,
= ( )
( )4.6 m for Jointed Plain Concrete Pavement JPCP
12.5 m for Jointed Reinforced Concrete Pavement JRCP
0.15 = Fault width in m
Figure C-17 Profile of a Faulted Rigid Pavement
h
0.15 m l
C-38
The resulting road profile over 1.6 km is:
( ) ( ) ( )0
1f
f
N
ju u urx x x jl
=
−+= −∑ (C.27)
Where, u(x) = Road profile
ur(x) = Road profile due to roughness
uf(x) = Roughness features (Equation C.25)
Nf = Number of faults per 1.6 km
Artificial Generation of Breaks/Bumps Break is a broken portion of the pavement section that starts with a negative fault and
ends with a positive fault. The distance between the two opposite faults should not exceed 0.9 m
(3ft), see (Huang, 2003). The general form of a break is given by Equation C.28. Figure C-18
shows the form used to find the mathematical description of a break. The same equation is used
to generate bumps except that the magnitude is h instead of (– h).
( )
( )
16001
0 0.150.15
0.15 0.75
0.9 0.75 0.90.15
0 0.9
lNb
hx x for xbh for x
h x for x
for x l
u
=−
= − ≤ ≤
− ≤ ≤ − ≤ ≤ ≤ ≤
(C.28)
Where,
ub (x) = Road profile due to break
Nb = Number of Break per 1.6 km h = Break magnitude in mm l = Distance between breaks in m
C-39
Figure C-18 Profile of a Concrete Slab with One Break The resulting road profile over 1.6 km is:
( ) ( ) ( )0
1b
b
N
ju u urx x x jl
=
−+= −∑ (C.29)
Where, u(x) = Road profile
ur(x) = Artificially generated road profile due to roughness
ub(x) = Roughness features (Equation C.27)
Nb = Number of breaks per 1.6 km
Artificial Generation of Curling Curling is the distortion of a slab into a curved shape by upward or downward bending of
the edges. This distortion can lift the edges of the slab from the base leaving an unsupported edge
or corner which can crack when heavy loads are applied. Sometimes, curling is evident at any
early age. In other cases, slabs may curl over an extended period of time. Curling is described as
ellipse and its general form is given by Equation C.30. Figure C-19 shows the form used to find
the mathematical description of curling.
( )22
2( ) 1
16001
c
c
x lu x h
l
l LN
− = − × −
= ≤−
(C.30)
h
0.15 m 0.9 m
0.15 m
l
C-40
Where,
uc (x) = Road profile due to curling in mm
Nc = Number of curling per 1.6 km h = Curling magnitude in mm l = Curling width in m L = Joint spacing in m
Figure C-19 Profile of a Curled Concrete Pavement
The resulting road profile over 1.6 km is:
( ) ( ) ( )0
1cN
ju u ur cx x x jl
=
−+= −∑ (C.31)
Where, u(x) = Road profile
ur(x) = Road profile due to roughness
uc(x) = Roughness features (Equation C.29)
Nc = Number of curling per 1.6 km
Artificial Generation of Potholes A pothole is when a portion of the road material has broken away, leaving a hole. Most
potholes are formed due to fatigue of the pavement surface. As fatigue cracks develop they
typically interlock in a pattern known as "alligator cracking". Then, the pavements between
fatigue cracks become loose by continued wheel loads forming a pothole. The width of a pothole
h
l l/2
L
C-41
was taken as 0.3 m (1ft). The general form of a pothole is similar to that for curling and it is
given by Equation C.32. The only difference is the ellipse width. Figure C-20 shows the form
used to find the mathematical description of a pothole.
( ) ( )22
16001
0.151 0 0.3
0.15
0 0.3
lN p
xx h for xp
for x l
u
=−
− = − × − ≤ ≤
≤ ≤
(C.32)
Where,
up (x) = Road profile due to potholes
Np = Number of Potholes per 1.6 km h = Pothole magnitude in mm l = Distance between potholes in m
Figure C-20 Profile of Asphalt Concrete Pavement with One Pothole
The resulting road profile over 1.6 km is:
( ) ( ) ( )0
1p
p
N
ju u urx x x jl
=
−+= −∑ (C.33)
Where, u(x) = Road profile
ur(x) = Road profile due to roughness
up(x) = Roughness features (Equation C.31)
Np = Number of potholes per 1.6 km
h
0.3 m 0.15 m
APPENDIX D
AN OVERVIEW OF EMERGING TECHNOLOGIES
D-2
INTRODUCTION
Background
In recent years, growing world population and the increase demand for road
transportation (with its associated energy requirements that are primarily derived from fossil
fuels) has led to the consideration, design and development of energy efficient vehicles and
processes. Furthermore, to realize improvements in energy efficiency (wrt road transportation)
the various engineering processes and/or technologies that have been developed range from -
drag reducing vehicle designs; intelligent vehicle operating technologies, e.g., adaptive cruise
control; the use of alternative energy sources, e.g., electricity; or intelligent transportation
systems that facilitate wireless communication between vehicles and transport infrastructure.
These and other technological interventions seek to assist in the reduction of vehicle operating
costs (VOC) and fossil energy consumption as well as minimize carbon footprints. This appendix
seeks to give an overview of emerging vehicle technologies that are set to play a major role in
reducing automobile VOC and also have the potential to mitigate negative environmental
impacts.
The highly competitive nature in the quest to develop energy efficient vehicles and/or
technologies that lower VOC has created an environment where intellectual property is heavily
guarded so not to precipitate a competitive disadvantage. Several unsuccessful attempts were
made to contact Research and Development (R&D) units of original equipment manufacturers
(OEM) to understand current and future vehicle, processes and technological developments. The
emerging technologies presented in this appendix are those where information about them are
publicly available, gained from published reports, slide presentations and/or online sources.
Vehicle Operating Costs
VOC are costs that arise from the direct use/operation of a vehicle and typically
comprise, gas, oil, maintenance and tires (all of which directly impact the other). VOC differ
from vehicle ownership costs such as insurance, licensing, registration, local taxes and financing
charges. Together (i.e., expenditures wrt operation and ownership) these costs represent the total
driving cost of operating and owning a vehicle. VOC can be measured in several ways, such as
cost per vehicle-mile or passenger-mile. According to the American Automobile Association
D-3
(AAA) for the year 2010, VOC costs per mile (excluding ownership costs) range from 14.10
cents to 19.31 cents dependent on the size of the vehicle (see Table D-.1). The figures in Table
D-1 generally indicate that as vehicle size increase so too do VOC. However, in order to
accommodate the growing demand for vehicles with low VOC, several OEM in recent years
have introduced innovative fuel efficient automobiles, e.g., Toyota Prius.
On average gas and oil costs account for 70 percent of VOC. Typical proportions of
these two VOC contributors are presented in Table D-2. It is also known that “the fuel
consumption of a vehicle is proportional to the forces acting on the vehicle. These forces are
rolling resistance, gradient, inertia, curvature and aerodynamic forces,” (Zaabar and Chatti,
2010) Thus, the dominance in the contribution of gasoline/fuel costs to VOC has precipitated the
drive to develop and utilize alternative fuels alongside an increasing use of technology in vehicle
operations.
Table D-1 2010 Vehicle Operating Costs Cents per Mile (by vehicle size)
Operating Item Small Sedan Medium Sedan
Large Sedan Sport Utility Vehicle 4WD
Minivan
Gas 9.24 11.97 12.88 16.38 13.70 Maintenance 4.21 4.42 5.00 4.95 4.86 Tires 0.65 0.91 0.94 0.98 0.75 Total 14.10 17.30 18.82 22.31 19.31 Source: American Automobile Association - Your Driving Costs (2010) Table D-2 Cost Components for Owning and/or Operating a Vehicle Costs Detail Average
Proportion (%) of per mile Cost*
Vehicle technologies impacting this cost component covered in this overview
Operating Costs
Gas & Oil 70.0 Yes Maintenance 26.0 Yes Tires 4.0 Yes
Ownership Costs
Insurance No License & Registration No Depreciation No Financing No
*see Table D-1
D-4
Vehicle Operation Energy Wastage
Table D-2 indicated that typically 70 percent of VOC is derived from gas/oil
requirements to propel the vehicle. Once this fuel requirement is fulfilled approximately 15
percent of the energy generated is actually used to move the vehicle (i.e., provide power to the
wheels) in urban conditions the rest is wasted. Furthermore, 67 percent of potential energy that
can be derived from fuel is lost while converting heat into mechanical work at the engine
(Transportation Research Board (TRB) Special Report, 2006). Figure D-1 schematically
represents automobile component energy uses and losses for urban driving environments. Figure
D.1 also reinforces the current trend of automobile engineering and/or technology research that
focuses on minimizing engine and drive train energy losses simultaneously increasing fuel
efficiency. Indeed, “there are various ways to increase vehicle fuel economy. Among them are
reducing the loads that must be overcome by the vehicle and increasing the efficiency of its
engine, its transmission, and other components that generate and transfer power to the axles.”
(TRB Special Report 286, 2006) The majority of emerging vehicle technologies reviewed in this
report focus on vehicle propulsion and alternative fuels when compared to technologies reducing
road surface friction (i.e., indirectly road roughness).
Source: TRB Special Report 286 (2006)
Figure D-1 Energy Uses and Losses for a mid-sized Passenger Car (operated in urban conditions)
D-5
Vehicle Technologies Roadmap
The drive for increased energy efficiency in an environment of finite/non-renewable
resources has spurred R&D institutions explore the use of alternative regenerative fuels,
materials of complex chemical makeup and/or sustainable processes to meet current and future
mobility needs. As knowledge and application boundaries are breached new challenges arise
necessitating continued research and development. The commercialization of vehicles using
hydrogen is a case in point a development which could take place at some point in the future.
Figure D-2 schematically encapsulates the current and potential development over time of
vehicle propulsion technologies.
Observation of Figure D-2 indicates that as time progresses and gasoline (for vehicle
propulsion) is displaced in favor of alternative fuels commercial use by automobiles using these
alternative fuels will increase. The use of alternative fuels in vehicle propulsion will bring
significant VOC savings but the storage and the safe integration into vehicles, fueling
infrastructure and vehicle cost are challenges that still require many more years of R&D to be
completely overcome. Indeed, the U.S. Energy Secretary Steven Chu stated in 2009 that “cars
powered by hydrogen fuel cells will not be practical over the next 10 to 20 years.” (Wald, 2009)
Figure D-2 Advanced Propulsion Fuels/Technologies R&D Timeline
D-6
ENGINE AND COMBUSTION TECHNOLOGIES
Sixty two percent of fuel energy lost in the propulsion of an automobile is attributable to
the engine (see Figure D-1). Thus, technologies that can increase engine efficiency whilst
reducing energy wastage will contribute to the overall lowering of VOC. The following section
provides an overview of emerging engine and [internal] combustion technologies.
Engine Friction Reduction
Engine friction is defined as “the part of mechanical efficiency lost to friction in such
engine components as bearings and rods” (National Highway Traffic Safety Administration
(NHTSA), 2009). The valve train and pistons for example are among several components of the
internal combustion engine (ICE) that are significant sources of engine friction and heat. Two
general approaches are used in reducing engine friction; namely, ‘asperity’ or ‘viscous.’ The
asperity approach seeks to reduce the roughness of surfaces that in turn reduces the amount of
friction between reciprocating and rotating components in the engine. On the other hand, the
viscous approach, seeks to reduce friction between two surfaces through the viscous properties of
fluids (i.e., the lower the viscosity of a fluid the greater its ease of movement). This can be
achieved through coating moving parts with friction reducing agents/additives. Another engine
friction reducing approach involves the redesign of engine components, e.g., shortening the
distance moving parts travel; roller cam followers; piston surfaces and rings, crankshaft design,
etc. Computer modeling continues to play a major role in the design and testing of engine
friction reduction technologies.
Gasoline Direct Injection (GDI)
GDI is a process where air is drawn into cylinder during the intake stroke and highly
pressurized gasoline is injected directly into the combustion chamber during this event. Through
this method the combustion process/engine load (that in turn controls engine power output) can
be more tightly controlled and stabilized leading to increased engine performance, fuel efficiency
and exhaust energy, in addition to lowering exhaust emissions. VOC gains are achieved through
eliminating “the substantial throttling energy losses associated with load control achieved by
restricting the intake air and fuel charge in current spark-ignited engines.” (Basic Energy Science
Workshop, 2006) Currently, Audi and Hyundai are two of several OEMs that have harnessed this
D-7
technology in the production of V6 engines. This intervention has the potential to realize an
increase in engine efficiency of up to 12 percent. (fueleconomy.gov, 2010)
Engine Downsizing
Engine downsizing “involves a substitution of a naturally-aspirated engine by an engine
of smaller swept volume.” (Energy Technology System Analysis Program (ETSAP), 2010) In
this procedure downsizing to a smaller engine may contribute to a reduction of its power output,
if not corrected. Correcting this potential deficiency to maintain torque and power output can be
achieved through turbocharging (i.e., boosting technology). VOC benefits of engine downsizing
are realized through: 1) increased engine efficiency requiring less fuel; 2) lower engine weight
contributing to a lower overall weight of the vehicle and 3) lower frictional losses as a result of
the smaller components within the engine itself. Several European OEMs produce models with
downsized/turbocharged engines, e.g., Audi (Audi S4) and Volkswagen (VW GTI 1.4 liter TSI).
This intervention coupled with turbocharging has the potential to realize an increase in engine
efficiency of up to 7.5 percent. (fueleconomy.gov, 2010)
Variable Valve Actuation (VVA)
VVA involves the alteration of the lift extent (i.e., variable value lift systems (VVL)) or
the duration and/or air intake timing (i.e., variable valve timing systems (VVT)) of valves within
ICEs. The control of these events significantly impacts engine performance and efficiency. “In a
standard engine, the valve events are fixed, so performance at different loads and speeds is
always a compromise between drivability (power and torque), fuel economy and emissions. An
engine equipped with a variable valve actuation system is freed from this constraint, allowing
performance to be improved over the engine operating range.’ (Wikipedia, 2010) This
intervention has the potential to realize an increase in engine efficiency of up to five percent.
(fueleconomy.gov, 2010)
Cylinder Deactivation
Simply put, cylinder deactivation enables cylinders within the ICE to be deactivated in
light load situations (i.e., low speeds or cruising). This type of intervention usually occurs in
large capacity ICEs, e.g., V6, V8+. Using a V8 engine as an example, in light load situations
D-8
such an engine would use four cylinders instead of the normal eight. With respect to heavy load
situations (i.e., acceleration, traveling uphill) all eight cylinders would be used. VOC benefits
arising from this intervention translate into improved fuel efficiency. This intervention has the
potential to realize an increase in engine efficiency of up to 7.5 percent. (fueleconomy.gov,
2010)
Variable Compression Ratio (VCR)
Compression ratios in standard gasoline ICEs are fixed - a compromise between engine
demands at high or low speeds/loads. The principle of the VCR ICE permits compression ratios
to vary according to the demands placed on the engine. Permitting variation in compression
ratios enables an improvement in the combustion process which in turn improves fuel efficiency
and reduces VOC. Further improvement in VOC benefits of VCR engines can be achieved
through engine downsizing and boosting (i.e., turbocharging).
Homogeneous Charge Compression Ignition (HCCI)
In HCCI ICEs a spark (often generated through an electrical discharge) is not used to
commence the combustion process, instead, compression of air within the cylinder causes the
fuel mix to ignite spontaneously. Compression raises the temperature in the combustion chamber
to a level where the fuel mix can ignite spontaneously. VOC benefits of the HCCI ICE can be
realized through increased fuel efficiency and lower throttle losses, however, these benefits need
to be offset by the difficulty of such engines to efficiently operate over a range of engine speeds
and loads.
Integrated Starter/Generator Systems (ISG)
ISG automatically switch-off the engine when idling and immediately restart it once the
gas pedal is pressed. This saves on the use of fuel while the engine is idling during extended
stops at traffic signals or when operating in stop-go operations (i.e., often experienced in heavily
congested traffic environments). VOC benefits arise from reduced fuel usage on trips where
extended stops are prevalent. Several European based OEMs (e.g., BMW, Fiat and Volkswagen)
have introduced this technology into a selection of their models.
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Continuously Variable Transmission (CVT)
Standard transmissions use gearsets that govern the ratio between the engine and wheel
speed. These gear ratios are fixed (i.e., usually, 4, 5 or 6) and to move between them gear
changing, initiated by the driver, is required. In the typical automobile, the lowest gears are used
for starting out, middle gears for acceleration and passing, and higher gears for fuel-efficient
cruising. CVT technology uses a pair of pulleys connected by a belt or chain, instead of typical
metal gears. This arrangement permits an infinite number of engine/wheel speed ratios be
obtained. CVT engines are more precise in optimizing engine speed to power output in different
driving conditions and this operation results in significant fuel consumption savings. Other VOC
savings arise from smoother acceleration/deceleration and less “gear hunting” when the vehicle
is traveling up or down hills (these latter benefits arise when compared to automatic transmission
vehicles). Several U.S. based OEMs have models using CVT technology such as, Dodge
Caliber, Jeep Compass, and Saturn Vue, etc. This intervention has the potential to realize an
increase in engine efficiency of up to six percent. (fueleconomy.gov, 2010)
Automated Manual Transmission (AMT)
AMT ICEs engage the engineering elements of manual and automatic transmission
systems. This form of transmission is also referred to as semi-automatic or clutchless manual
transmission. In this system a clutch pedal is not required to change gears, instead electronic or
hydraulic systems are used, initiated by the driver. Electronic or hydraulic systems allow precise
gear change to optimize torque and timing, an advantage of this system over that of manual
transmission. The recently introduced micro vehicle the Smart For Two employs AMT
technology (see Figure D-3). Vehicles employing AMT technology have the potential to realize
an increase in engine efficiency of up to seven percent. (fueleconomy.gov, 2010)
Figure D-3 The Smart for Two (an automobile that uses Automated Manual Transmission Technology
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Six+ Speed Gearboxes
Gear boxes permitting 6 or more gear ratios/transmissions are available in manual or
automatic automobiles. A higher number of gears (from the standard 4 or 5) permits finer tuning
of the engine torque to optimal power output to the wheels required under different driving
conditions. In other words, automobiles which have 6 or more gears attain a better match of
engine speeds in the correct gear since there are more gears to choose from. This in turn
optimizes engine efficiency contributing to VOC savings. Several OEMs offer 6+ speed
automobile models, e.g., Chrysler, Ford and Toyota. Audi, BWM and Lexus also offer (or will
offer in 2011) 8 speed models.
ALTERNATIVE FUELS AND Technologies
Recent years have realized a greater awareness of the finiteness of energy resources
derived from fossil fuels. Alternative and sustainable energy resources are being explored and
developed for powering transportation vehicles. Alternative fuels and their associated
technologies are presented in this section.
Vehicles Powered by Natural Gas
Natural gas often in the form of compressed natural gas (GNG) can be used as an
alternative to regular gasoline to power vehicles. Advantages of using this fuel source are its
clean burning quality as well its wide availability domestically (i.e., in homes) in the U.S.
However, several barriers exist in the U.S. for the widespread take-up of CNG powered vehicles,
some of which are, limited traveling range, reduced trunk space and lack of refueling
infrastructure. Overall, there has not been a proliferation of vehicles fueled by CNG in the U.S.
Nevertheless, transit buses have applied this technology (e.g., New Flyer Manufactured buses
operated by the Washington DC Metropolitan Area Transit Authority). With respect to
automobiles the Honda Civic GX (shown in Figure D-4) is the only commercially CNG vehicle
available but only sold in four states of the U.S., namely, California, New York, Oklahoma and
Utah.
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Figure D-4 The Honda Civic GX (an automobile powered by Compressed Natural Gas)
Vehicles Powered by Electricity
The electric vehicle (EV) uses one or more electric motors for propulsion working in
tandem with rechargeable battery packs. Electric vehicles have been around for some time and
their potential as a viable alternative to gasoline has increased in recent years due to
environmental sustainability and energy security concerns. VOC benefits and disadvantages of
EVs can be listed as:
Advantages
• When the vehicle is not moving, energy consumption is minimal or absent • Quiet and smooth operation reducing wear, tear and vibration effects on vehicle
Disadvantages
• Bulk, weight and onboard storage of fuel cells • Limited travel range, therefore, journeys require more recharge stops
Vehicles Powered by Hydrogen
Exploring the use of alternative fuels to power automobiles, hydrogen, is also seen as a
potential fuel source. Propulsion using hydrogen can be achieved by storing this gas in onboard
fuel cells which power electric motors or by burning hydrogen directly in an ICE. Vehicles
powered by hydrogen are still in the R&D phase (i.e., demonstration fleets) and there are several
challenges to overcome before the hydrogen vehicle (HV) can enter into commercial production.
Potential VOC benefits and disadvantages of HVs can be listed as:
D-12
Advantages
• High levels of conversion energy are achieved (i.e., relationship between each unit of fuel consumed and the resulting energy produced)
• When the vehicle is not moving, energy consumption is minimal or absent • Quiet and smooth operation reducing wear, tear and vibration effects on vehicle
Disadvantages
• Bulk and weight of fuel cells • High initial cost could result in higher lifetime VOC • Limited travel range, therefore, journeys require more refuelling stops
Vehicles Powered by Biodiesel
Biodiesel is a renewable fuel derived from vegetable oils (e.g., soybean, canola), animal
fats, or recycled restaurant grease (i.e., yellow or brown). Biodiesel can only be used in diesel
engines the majority of which are found in trucks and buses. Biodiesel has varied levels of purity
ranging from 100 percent (i.e., pure B100), B2, B5 and B20 (i.e., 2, 5 and 20 percent Biodiesel
respectively blended with regular diesel). VOC savings through the use of biodiesel are
marginal, i.e., it is a performance enhancer to conventional diesel due to its higher lubricity
levels. However, increases in VOC may arise from 1) retail biodiesel prices tend to be higher
than regular diesel; 2) biodiesel blends higher than 5 percent may have the potential to negatively
impact engine durability; and 3) a lower fuel economy and power output compared to diesel
(10% lower for B100, 2% for B20) (fueleconomy.gov, 2010)
Ethanol Fueled Vehicles
Ethanol is an alcohol and can be used as an additive to regular gasoline to power
automobiles. Indeed, most automobiles can use regular gasoline containing up 10 percent ethanol
without any engine modification. However, vehicles using gasoline blends where the ethanol
proportion is higher than 10 percent (up to 85 percent) are termed Flex Fuel Vehicles (FFV) and
require a slightly modified engine. VOC savings through the use of ethanol are marginal, i.e., the
retail price of ethanol in some parts of the U.S. is typically lower than gasoline. However,
increases in VOC may arise from 1) low energy content of ethanol resulting in fewer miles per
gallon (see Table D-3); 2) increasing internal wear of electric pumps (Wikipedia2, 2010) and 3)
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higher initial cost of FFV could result in higher lifetime VOC. The limited regional availability
(in a U.S. context) of ethanol may result in FFVs not maximizing potential VOC savings. Table
3 presents miles per gallon and corresponding annual fuel costs for a selection of regular
gasoline and flex fuel vehicles.
Table D-3 2010 Flex Fuel Vehicles and estimated Annual Fuel Cost* Model Gasoline FlexFuel
MPG (city) Annual Fuel Cost
MPG (city) Annual Fuel Cost
1 Chevrolet Malibu 22 $1,571 15 $2,018 2 Pontiac G6 19 $1,775 14 $2,175 3 Chevrolet Impala 18 $1,856 14 $2,134 4 Chrysler Town &
Country 17 $2,146 12 $2,719
5 Ford Crown Victoria 16 $2,146 12 $2,592 Source: Fueleconomy.gov
Hybrid Vehicles
The term hybrid in an engineering context may refer to an automobile that has two
components that produce the same or similar results. This type of technology has been applied to
automobiles for more than 10 years. With respect to the hybrid electric vehicle (HEV) this would
be the combination of a conventional gasoline ICE together with an electric motor. In HEVs
both the gasoline engine and electric motor are used as energy sources for the drive train.
Additionally, supplementary technologies are often used to maximize VOC savings in HEVs,
e.g., rechargeable batteries, ISG systems and regenerative braking, etc. VOC savings are realized
through:
• Electric motor assists gasoline ICE during peak power needs, i.e., acceleration phases, thus, a smaller fuel efficient ICE is required
• During idling, the electric motors provide power allowing the ICE to shutdown • Batteries allow energy to be stored to be re-used to assist the ICE
Currently, more than two dozen HEVs models are produced for the U.S. automobile
market. Fuel cost and miles per gallon data for a selection of these vehicles are presented in
Table D-4. A derivative of the HV is the plug-in hybrid electric vehicle (PHEV) which having
larger battery packs can recharge by connecting directly to domestic household electricity
supply.
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Table D-4 Top Five 2010 Hybrid and Gasoline Small Vehicles Rank Hybrid Conventional/Gasoline
Model MPG Annual Fuel Cost (city)
Model MPG Annual Fuel Cost (city)
1 Toyota Prius 51 $816 Toyota Yaris
(manual)
36 $1,277
2 Ford Fusion 41 $1,044 Toyota Yaris
(automatic)
35 $1,318
3 Mercury Milan
41 $1,044 Honda Fit 35 $1,318
4 Honda Civic 40 $971 Hyundai Accent
34 $1,359
5 Honda Insight
40 $996 Kia Rio 34 $1,318
Source: Fueleconomy.gov
VEHICLE DESIGN & MAINTENANCE
Regenerative Braking Systems (RBS)
Regenerative braking is the process where the energy used in slowing down the vehicle
(i.e., while braking) is recycled through an electric motor to further slow the vehicle down or
stored in the battery. In conventional vehicles the kinetic energy created during the braking
process dissipates as heat, however, with RBS this same energy is harvested through reversing
the electric motors. Instead of the electric motors propelling the vehicle forward the braking
energy from the wheels reverse this relationship. Thus, the torque created by this reversal
counteracts forward momentum and assists in stopping the vehicle. RBS are commonly found in
HVs equipped with battery packs such as the Toyota Prius.
Electric Motor Drive/Assist (EMD)
Electric motor drive/assist technology is an intervention where an electric motor assists
an ICE through the provision of additional power as and when needed, e.g., during accelerating
or hill climbing. This intervention allows a lower swept capacity of an ICE to be achieved (i.e., a
smaller engine). Several models of HVs employ this type of technology, e.g., Honda Insight.
VOC benefits are similar to engine downsizing as described earlier.
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Lightweight Materials
Vehicle weight is a significant contributor to VOC, fuel consumption and vehicle
performance. In fact, “75% of vehicle gas (energy) consumption directly relates to factors
associated with vehicle weight.” (Oak Ridge National Laboratory (ORNL), 2010) In recent years
there has been continued research in the use of alternative lightweight materials in vehicle
building instead of traditional steel. The use of these new materials, though lightweight, aim not
to compromise material strength characteristics, environmental impact, financial viability and
vehicle performance and safety, as well as occupant safety characteristics of vehicles made with
materials such as steel. Lightweight materials that have shown promise are: aluminum,
magnesium, titanium, advanced high-strength steels, fiber-reinforced composites, and metal
matrix composites. VOC benefits arise from increased fuel efficiency and smaller powerplant
requirement (e.g., ICE or fuel cell, etc.) as a result of reduced vehicle weight.
Vehicle Aerodynamics
As an object moves through air it creates a disturbance (or instability) which can be
manifested as drag, wind and/or vehicle noise and unwanted lift. The extent of drag/air friction
significantly impacts fuel efficiency and vehicle performance and tends to increase as vehicle
surface area and/or speed increase. Thus, vehicle aerodynamics seeks through vehicle design to
minimize or eradicate aerodynamic instability of a vehicle as it travels. Smother vehicle shapes
can significantly contribute to drag reduction. The drag coefficient (Cd) is a measure of a
vehicle’s aerodynamic smoothness and it was projected that Cd typically ranges from 0.25 to
0.35. In the U.S. this vehicle characteristic has generally fallen in the current decade as vehicles
have become smaller and more fuel efficient due to the increased fuel efficiency requirements of
new vehicle designs and a growing retail market for smaller vehicles. For example, in 2003 a Cd
= 0.57 was associated with the Hummer H2 compared to a Cd = 0.25 for the 2009 Toyota Prius
(model ZVW30) (see Figure D-5). Automobile designers can improve aerodynamic
characteristics vehicles by:
• rounding the edges of the front end • adjusting the grille and fascia openings • installing small spoilers in front of the tires to reduce turbulence • adjusting the size and shape of the outside mirrors and their attachment arms • adding side skirts
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• installing a rear spoiler • adjusting the angle of the rear window • tucking up the exhaust system • installing underbody panels that cover components and smooth airflow
VOC benefits arising from improved aerodynamics can significantly increase fuel
efficiency and vehicle performance.
Figure D-5 The Hummer H2 (2003) and Toyota Prius ZVW30
Intelligent Transportation Systems (ITS)
ITS involve the application of information and communication technology to
transportation infrastructure or vehicles. With respect to lowering VOC, in-vehicle ITS
technologies can take the form of:
• Cruise Control Systems (CCS) are systems that maintain a selected cruising speed without the need to constantly depress the gas pedal
• Adaptive Cruise Control Systems (ACC) that maintain a minimum lead distance between the lead and following vehicle
• Global Positioning Systems (GPS) or mapping systems that can identify the shortest distance between two points or indicate the slowest, fastest or congested route enabling a driver to make informed choices as to route choice
• Fuel Efficiency Gauge that allows the real-time monitoring of mpg while traveling • Maintenance Required Gauge that alerts the driver when a preventative maintenance check
is due
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TIRE TECHNOLOGIES
Research has shown that tires through their rolling resistance are directly responsible for
approximately four percent of energy losses in the typical automobile (see Figure D-1). It is also
known that underinflated tires contribute to lower fuel economy as do vehicle load and erratic
vehicle handling. Ongoing research efforts continue to develop tire technologies that primarily
address tire inflation (and indirectly rolling resistance) as described below.
Tire Pressure Monitors Systems (TPMS)
Air pressure in tires needs to be continually monitored as pressure decreases over time
and use precipitating changes in VOC. In fact, “under normal driving conditions, air-filled tires
can lose from 1 to 2 psi per month as air permeates through the tires.” (Government
Accountability Office (GAO), 2007) TPMS monitor the air pressure of pneumatic tires and
report this information back to the driver by way of an indicator on the vehicle dashboard. TPMS
generally are of two types; direct and indirect. Direct TPMS monitor pressure directly from
inside the tire compared to indirect TPMS which monitor differentials between individual
rotational wheel speeds (i.e., rotational wheel speed is influenced by the air pressure inside the
wheel). If the pressure in any individual wheel falls below a government regulated minimum
threshold or an OEM specified level a warning signal is sent to the driver to take corrective
action, i.e., inflate the tire to its OEM recommended pressure. Recently, NHTSA has required
that all new automobile models from 2008 are equipped with TPMS. Maintaining tires at their
correct pressure optimal mpg can be achieved.
Tire Innerliners
A tire innerliner is a sheet of rubber that is used to line the inside of a tire casing. This
sheet typically is made from varying blends of synthetic rubber and other chemical additives
with the purpose of reducing the potential of air escaping through the tire structure. Different
chemical additives, e.g., bromobutyl have been shown to improve the air retention characteristics
and durability of a tire casing. Other tire innerliner technologies have sought to reduce the weight
of the tire casing through the use of innovative tire innerliner materials, e.g., . Exxcore™ DVA
of the ExxonMobil corporation. As air leakage through the casing wall is reduced tires are able
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to maintain their correct pressure for longer assisting in minimization of engine-wheel energy
losses.
Nitrogen
Recently, the use of nitrogen has been promoted as an alternative gas for filling tires
especially with respect to truck tires. Slower permeation of rubber (i.e., the tire casing) than
regular air is the primary benefit of using nitrogen in tires. In such a case, nitrogen filled tires
should maintain tire air pressure and durability for longer periods of time. VOC benefits are
similar to those achieved from tire innerliners.
Low Rolling Resistance Tires
Low rolling resistance tires seek to reduce the energy wasted as heat as the tire rolls over
a surface (i.e., energy is lost due to tire deformation as it passes over a surface or is
underinflated). “It is estimated that 5%-15% of light-duty fuel consumption is used to overcome
rolling resistance for passenger cars.” (U.S. Department of Energy (DOE), 2010) By conserving
the energy wasted from tire rotation less energy is required to move the tires and so VOC
benefits can be realized. A lower rolling resistance in tires may be achieved by correct air
pressure and/or a stiffer tire casing. Typically, HVs are equipped with low rolling resistance
tires that contribute to their high fuel efficiency. However, such tires are usually more inflated
than regular tires and stiffer.
In order to maintain the VOC savings of low rolling resistance tires (which often come
with new vehicles) consumers require detailed information about the fuel saving properties of
aftermarket tires. Currently, this information is difficult to find as the provision of this tire
statistic is not a government mandated requirement with respect to aftermarket retail sales.
However, the California Energy Commission continues to undertake extensive research of the
rolling resistance properties of tires and codifying results into a format that will enable
consumers to make informed choices when purchasing aftermarket tires.
CONCLUSION
The continued push for fuel efficient vehicles coupled with growing demand for them has
accelerated the R&D efforts to meet this need. This development has prompted the harnessing of
D-19
alternative fuels in vehicle propulsion, redesign and reevaluation of combustion and propulsion
processes and a reassessment of how vehicles interact with their immediate environment in terms
of their environmental impact, aerodynamic/pavement friction efficiency, congestion impacts,
etc. All of the technologies presented in this report have the potential to lower VOC.
Nevertheless, the majority of current R&D efforts focus on engine and combustion technologies
(including alternative fuels) which have the potential to significantly reduce energy loss of
vehicle operation. Moving into the future, the cost of retrofitting existing fleets and the take up
and affordability of new vehicles with these technologies will determine the extent, sustainability
and reality of predicted VOC savings to individual motorists and society.
This appendix presented the results of the literature review and summarized the collected
information regarding the emerging technologies that lower VOCs. To summarize, the new
technologies that were proven to affect vehicle operating costs are: the engine and combustion
technologies, alternative fuels and technologies, vehicle design & maintenance, and tire
technologies. The last two emerging technologies will affect the effect of pavement conditions
on VOCs.